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How do responses vary between mothers and their daughters on measuring daughter’s self-rated health (SRH): a study among school-going adolescent girls in the primary setting of Varanasi, India

Abstract

Objective

How self-rated health (SRH) varies when the response on SRH is recorded from the respondent herself (adolescent girl) and her mother on her behalf. This study examines the prevalence of SRH among adolescent girls from her point of view as well as from her mother’s point of view. This insight could help us interpret the differences in opinion of girls and their mothers while measuring the girls’ self-rated health.

Results

Almost one-fifth (19.4%) of the girls reported poor SRH. In contrast, only one in eight mothers (12.3%) could report their daughters under the category of poor SRH. Nearly one-third (76.5%) of the mothers reported their daughter’s SRH as good when daughters themselves rated poorly on SRH and another one-tenth (9.6%) reported their daughter’s SRH as poor when daughters themselves categorized in the good SRH category [χ2 = 9.900; p < 0.002]. More than 90 percent of the Rich and Middle wealth index women, women in the household with only daughters and no son, women whose husbands had higher education, women with higher secondary education, and non-working women visualized their daughter’s SRH as good when daughters themselves reported poor SRH.

Introduction

There is a widespread consensus in the available literature that self-rated health (SRH), measured as a one-item general health question, is one of the important health indicators with widespread applicability [1, 2]. Despite its widespread applicability, hardly any other measure of health is more poorly understood than self-rated health [1]. This measure is based on asking individuals to evaluate their health status and is being frequently employed in sociological health research since the 1950s [2,3,4]. The question on self-rated health is so prominent that the data on SRH are collected in major national and international surveys including World Value Survey, European value Survey, and Longitudinal Ageing Study in India, and recommended as a standard part of health surveys [5].

Self-rated health is a well-known and reliable indicator to measure the health status of the population [6], even in India [7, 8]. It has been studied widely across countries [9,10,11,12,13,14,15,16,17] and sub-populations including older adults [18,19,20,21,22,23,24], adults [25,26,27,28], and adolescents [29,30,31,32,33]. Somehow, studies examining factors associated with self-rated health among adolescents remained unearthed topic in the Indian context. Furthermore, how self-rated health varies when the response was recorded from the respondent herself (adolescent girl) and was asked from her mother on her behalf. This study is unique in the sense that the self-rated health was asked from adolescent girls and their mothers depicting the possible discordant between a mother and her daughter. Therefore, this study examines the prevalence of self-rated health among adolescent girls from her point of view as well as from her mother’s point of view. This insight could help us interpret the differences in opinion of girls and their mothers while measuring the girls’ self-rated health and also presents possible correlates of poor self-rated health among adolescent girls.

Main text

Data and methods

This study is based on the primary data collected in the Varanasi district of Uttar Pradesh, India, from October 2019 to February 2020. Nearly 350 adolescent girls and their mothers were personally interviewed. The purpose of the primary survey was to examine the self-rated health status of the adolescent girls and then compare the response with that of mothers’. While framing the question on self-rated health, it was hypothesized that mothers might not be well informed about their daughter’s self-rated health and therefore any deviation between the responses of mothers and daughters on self-rated health would provide discordance in mothers’ responses on self-rated health.

Sample size estimation

The adolescent consists of children in the age group 10–19 years of age. Adolescent can be divided into three various groups based on their age group namely; early adolescent (10–12 years), middle adolescent (13–16 years), and late adolescent (17–19 years). This study is based on middle and late-adolescent girls. The study was conducted on school-going girls (8th standard to 12th standard) in the age group between 13 and 19 years of age.

For taking prevalence, the number of literate girls in the urban area of Varanasi, as per census 2011, in the age group 13–19 are taken as the numerator and total girls in the age group 13–19 are taken as the denominator.

$${\text{p}} = \frac{{Number{\mkern 1mu} \,of\,{\mkern 1mu} literate\,{\mkern 1mu} girls\,{\mkern 1mu} in{\mkern 1mu} \,the\,{\mkern 1mu} age\,group\,{\mkern 1mu} 13{{{-}}}19\,years\,{\mkern 1mu} in\,{\mkern 1mu} urban{\mkern 1mu} \,Varanasi}}{{Total\,girls\,{\mkern 1mu} in\,{\mkern 1mu} the\,{\mkern 1mu} age\,group\,13{{{-}}}19\,years\,{\mkern 1mu} in\,{\mkern 1mu} urban{\mkern 1mu} \,Varanasi}}*100,$$
$${\text{p}}=\frac{103373}{120986}*100,$$

p = 85.44.

The sample size estimation for the study is done by using the formula developed by Cochran (1977). The formula is as follows:

$${\text{n}}=\frac{\left(z\right)2*p*q}{\left(d\right)2}$$

where, n = Required Sample Size; Z = 1.96 (95% level of confidence); p = 0.8544; q = 0.1456.

α = 0.05 (5% margin of error); n = 191.

By taking a non-response rate of 10 percent and a design effect of 1.5, the sample size was to be:

$${\text{n}} = {211}\,\,*\,\,{1}.{1}\,\,*\,\,{1}.{5} = {\mathbf{315}} \, {\mathbf{Individuals}}$$

So, nearly 350 adolescent girls from the school were interviewed.

Sampling design

Varanasi district is subdivided into five zones for ease of administration. A total of ten schools were selected, two from each zone (Wards). Out of ten schools, five public and five private schools were selected. Two schools, one public, and one private school were selected from each zone (wards). From each school, a total of 35 students were interviewed. These 35 students were selected from classes 8–12th. From each class, 7 students were selected for the interview.

Selection of school

Varanasi city is divided into five zones and zones are further divided into wards. One ward was selected from each zone randomly. After selecting five wards, one from each zone, a complete public, and private school listing was carried out. Two schools, one private and one public school were selected from each ward randomly. If in case, a ward is not having either of public or private school, the next ward was selected randomly. If in case, a school is not interested in participating in the study, the next school was selected randomly.

Selection of respondents from school

From each class, seven students were selected by employing systematic random sampling. For sampling, a complete list of students was taken from the class attendance register. The mothers of the selected student were personally interviewed in their households.

Inclusion criteria

  1. 1.

    Girls aged 13–19 years of age; and

  2. 2.

    Girls studying in class from 8 to 12th.

Exclusion criteria

  1. 1.

    Disabled girls were not interviewed; and

  2. 2.

    Those girls whose mothers are not alive were not interviewed.

Outcome variable

Self-rated health was the primary outcome variable of this study. SRH was a dichotomous variable where 0 means ‘Good SRH’ and 1 means ‘Poor SRH.’ The exact wording of the question asked from the adolescent girls was “In general, how would you say your health is?” Similarly, the exact wording of the question asked from the mothers of adolescent girls was “In general, how would your daughter rate her health?” In both scenario, the SRH of the girl was asked, however from a different perspective. In the first case, a girl herself is reporting her SRH and in the second case, her mother is visualizing the SRH on her behalf. By doing so, we aimed at quantifying the mismatch in the response of SRH between mothers and their daughters.

Exposure variable

Exposure variables were divided into three groups; (1) Household Characteristics; Caste [Scheduled Castes/Scheduled Tribes (SC/ST), Others Backward Castes (OBC), and Others], Religion (Hindu and Non-Hindu), Wealth Index (Poorest, Poor, Middle, Rich, and Richest), and Composition of Children (Only daughter/no son, equal son and daughter, more son/less daughter, and more daughter/less son); (2) Parental characteristics; Father’s education level (No education, Primary, Secondary, Higher Secondary, and Higher Study), Mother’s education level (No education, Primary, Secondary, Higher Secondary, and Higher Study), Working status of father (Working and Not working), and Working status of mother (Working and Not working); and (3) Adolescent girl’s characteristics; Girl’s education level (8–10th and 11–12th) and Age of the girl (13–15 years, and 16–19 years).

Statistical analysis

The study uses bivariate analysis; and to depict the significance, a chi-square test was performed.

Ethical issues

The study proposal and survey questionnaires were approved by the Student Research Ethics Committee (SREC) of the institute. Written consent was taken from the individual respondents. Participation in the study was made voluntary, and participants were allowed to withdraw at any point during the interview if desired. Additional files 1 and 2 presents the adolescents’ and mothers’ questionnaires respectively.

Results

Table 1 depicts the percentage distribution of the sample by selected background variables along with depicting the prevalence of poor SRH as reported by girls and as visualized by their mothers by background characteristics. Almost one-fifth (19.4%) of the girls reported poor SRH for them. In contrast, only one in eight mothers (12.3%) could visualize their daughters under the category of poor SRH. Reporting of poor SRH varies between girls and their mothers by almost all the background characteristics as depicted in Table 1. The stark differences in reporting SRH by girls and their mothers were noticed for higher wealth index and higher educational categories.

Table 1 Percentage distribution of the selected sample of the girls, bivariate distribution of SRH as reported by adolescent girls and their mothers, and difference in reporting in SRH by girls and their mothers

Table 2 depicts the discordance in SRH as reported by girls and their mothers. Nearly one-third (76.5%) of the mothers visualized their daughter’s SRH as good when daughters themselves rated poorly on SRH and another one-tenth (9.6%) visualized their daughter’s SRH as poor when daughters themselves categorized in the good SRH category.

Table 2 Discordance in SRH as reported by daughters and their mothers

Table 3 shows the response of mothers on visualizing the SRH of their daughters when their daughters rated their SRH as poor. A significant proportion of mothers from all the given background characteristics visualized their daughter’s SRH as good when daughters themselves rated poorly on SRH. More than 90% of the Rich and Middle wealth index women, women in the household with only daughters and no son, women whose husbands had higher education, women with higher secondary education, and non-working women visualized their daughter’s SRH as good when daughters themselves reported poor SRH.

Table 3 Response of mothers on SRH when daughters reported poor SRH for themselves by various background characteristics

Discussion

This study examined the SRH among school-going adolescent girls by adopting a unique approach: probing the self-rated health of the girls by girls and their mothers. Firstly, a girl was asked to rate her health as either good or poor and then her mother was asked about how her daughter would rate her health. By doing so, this study explored the possible perception of mothers on how well they understand their daughter’s health. The study noticed that a significant proportion of mothers failed to understand their daughter’s perception of self-rated health. It is significantly evident from the findings that a large proportion of mothers visualized their daughter’s SRH as good when daughters themselves were categorized poorly on SRH. What could be the possible mechanisms and why mothers fail to recognize the health condition of their daughters is worth probing.

Family bonds have been characterized as an asset in health promotion intervention [34] and studies in the Indian context have clearly outlined the gender biases in the family towards the female gender from a health perspective [35]. This biasedness towards the female gender could be a possible attribute why mothers fail to correctly percept the poor SRH of their daughters as outlined in this study. In line with this discussion, the study also noted that in the case of an equal number of sons and daughters in the household, a significant proportion of mothers tend to visualize their daughter’s SRH as good when the daughter reported poor SRH. This finding weighs on a longstanding debate of gender inequality in households disfavouring females. There could also be another possible reason why mothers think that their daughters would rate their health as good when daughters rated their health as poor. Lundberg et al. [36] opined that boys as compared to girls are more likely to reap financial and emotional benefits from their parents, and this could be one of the plausible factors why in this study mothers failed to visualize poor SRH of their daughters.

A higher proportion of mothers from rich wealth quintile and higher education status visualized their daughters' SRH as good when daughters reported poor SRH. Highly educated mothers belonging to the rich wealth quintile are more likely to work and their career might demand long work hours leading to negligence in child care [37] and thereby it can be inferred that they might not be well aware of their daughter’s SRH as depicted in this study. Luthar and Latendresse [37] called this an antecedent of ‘isolation from adults’ where among upper-middle-class families, adolescent children often left home alone for several hours each week, giving a sense of self-sufficiency to parents that could have several emotional repercussions to children. Social support from family, specifical parents, plays an imperative role in the life of the adolescents and also is a critical factor for them to rate their self-rated health [38], and it is understood that in affluent families children might feel neglected [39]. This feeling of neglect might have driven adolescents to rate their health as poor, while mothers might be under the impression that belonging to the rich wealth quintile automatically helps them to visualize their daughter’s health as good.

Conclusion

A mother’s knowledge about children’s health is impromptu to the quality of care she provides to her children and therefore it becomes critical to seek improvements in mother’s involvement in the healthcare needs of their children. This study clearly outlined that mothers failed to visualise correct SRH for their daughters as discrepancies were noticed between mothers visualizing their daughter’s SRH and reporting of SRH by daughters themselves. The involvement of mothers in visualizing children’s health shall be promoted to avoid any serious complications. Mother-daughter relationships may be a potential asset to promote good self-rated health among adolescent girls. It is highly desirable to educate mothers on the importance of self-rated health of their daughters and therefore the importance of the mother-daughter relationship as a locus for health promotion is critical.

Strengths of the study

We could not find a single study that examined the discordance in the perception of self-rated health between daughters and their mothers in the Indian context. That way, this is quite an untouched domain and may pave the way for future research. The response of daughters and their mothers on SRH were collected following the guidelines of the widely used KIDSCREEN-52 scale.

Limitations of the study

Despite above-mentioned strengths, the study has some potential limitations. The study findings shall not be generalized at the national-level as the data were collected from one district only. Since SRH was a self-reported outcome, it might be affected by social bias and conformity.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available as this data is a part of corresponding author’s PhD research work and it was collected by corresponding author to receive the PhD degree, but are available from the corresponding author on reasonable request.

Abbreviations

SC/ST:

Scheduled castes/scheduled tribes

OBC:

Other backward class

SRH:

Self-rated health

References

  1. Jylhä M. What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc Sci Med. 2009;69(3):307–16. https://doi.org/10.1016/j.socscimed.2009.05.013.

    Article  PubMed  Google Scholar 

  2. Garrity TF, Somes GW, Marx MB. Factors influencing self-assessment of health. Soc Sci Med Part Med Psychol Med Sociol. 1978;12:77–81.

    CAS  Google Scholar 

  3. Maddox GL. “Some correlates of differences in self-assessment of health status among the elderly. J Gerontol. 1962. https://doi.org/10.1093/geronj/17.2.180.

    Article  PubMed  Google Scholar 

  4. Suchman EA, Phillips BS, Streib GF. An analysis of the validity of health questionnaires. Soc Forces. 1957;36:223–32.

    Article  Google Scholar 

  5. Robine JM, Jagger C, E-R Group. Creating a coherent set of indicators to monitor health across Europe: the Euro-REVES 2 project. Eur J Public Health. 2003;13(suppl_3):6–14.

    Article  Google Scholar 

  6. Lundberg O, Manderbacka K. Assessing reliability of a measure of self-rated health. Scand J Soc Med. 1996;24(3):218–24.

    CAS  Article  Google Scholar 

  7. Cullati S, Mukhopadhyay S, Sieber S, Chakraborty A, Burton-Jeangros C. Is the single self-rated health item reliable in India? A construct validity study. BMJ Glob Health. 2018;3(6): e000856. https://doi.org/10.1136/bmjgh-2018-000856.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Subramanian SV, Subramanyam MA, Selvaraj S, Kawachi I. Are self-reports of health and morbidities in developing countries misleading? Evidence from India. Soc Sci Med. 2009;68(2):260–5.

    CAS  Article  Google Scholar 

  9. Cela E, Barbiano di Belgiojoso E. Ageing in a foreign country: determinants of self-rated health among older migrants in Italy. J Ethn Migr Stud. 2021;47(15):3677–99.

    Article  Google Scholar 

  10. Noh J-W, Kim J, Yang Y, Park J, Cheon J, Kwon YD. Body mass index and self-rated health in East Asian countries: comparison among South Korea, China, Japan, and Taiwan. PLoS ONE. 2017;12(8): e0183881.

    Article  Google Scholar 

  11. Story WT, Glanville JL. Comparing the association between social capital and self-rated health in poor and affluent nations. SSM-Popul Health. 2019;9: 100508.

    Article  Google Scholar 

  12. Jerkovic OS, Sauliune S, Šumskas L, Birt CA, Kersnik J. Determinants of self-rated health in elderly populations in urban areas in Slovenia, Lithuania and UK: findings of the EURO-URHIS 2 survey. Eur J Public Health. 2017;27(suppl_2):74–9.

    Google Scholar 

  13. Takahashi S, Jang S, Kino S, Kawachi I. Gender inequalities in poor self-rated health: cross-national comparison of South Korea and Japan. Soc Sci Med. 2020;252: 112919.

    Article  Google Scholar 

  14. Duboz P, Boëtsch G, Gueye L, Macia E. Self-rated health in Senegal: a comparison between urban and rural areas. PLoS ONE. 2017;12(9): e0184416.

    Article  Google Scholar 

  15. Vincens N, Emmelin M, Stafström M. Social capital, income inequality and the social gradient in self-rated health in Latin America: a fixed effects analysis. Soc Sci Med. 2018;196:115–22.

    Article  Google Scholar 

  16. Hung N, Lau LL. The relationship between social capital and self-rated health: a multilevel analysis based on a poverty alleviation program in the Philippines. BMC Public Health. 2019;19(1):1641. https://doi.org/10.1186/s12889-019-8013-5.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Moreno X, Albala C, Lera L, Sánchez H, Fuentes-García A, Dangour AD. The role of gender in the association between self-rated health and mortality among older adults in Santiago, Chile: a cohort study. PLoS ONE. 2017;12(7): e0181317.

    Article  Google Scholar 

  18. Bardage C, et al. Self-rated health among older adults: a cross-national comparison. Eur J Ageing. 2005;2(2):149–58.

    Article  Google Scholar 

  19. Campos ACV, Albala C, Lera L, Sánchez H, Vargas AMD, e Ferreira EF. Gender differences in predictors of self-rated health among older adults in Brazil and Chile. BMC Public Health. 2015;15(1):1–11.

    Article  Google Scholar 

  20. Menec VH, Shooshtari S, Lambert P. Ethnic differences in self-rated health among older adults: a cross-sectional and longitudinal analysis. J Aging Health. 2007;19(1):62–86.

    Article  Google Scholar 

  21. Verropoulou G. Determinants of change in self-rated health among older adults in Europe: a longitudinal perspective based on SHARE data. Eur J Ageing. 2012;9(4):305–18.

    Article  Google Scholar 

  22. Lima-Costa MF, Firmo JOA, Uchôa E. Differences in self-rated health among older adults according to socioeconomic circumstances: the Bambuí Health and Aging Study. Cad Saúde Pública. 2005;21:830–9.

    Article  Google Scholar 

  23. Gracia E, Herrero J. Internet use and self-rated health among older people: a national survey. J Med Internet Res. 2009;11(4): e49.

    Article  Google Scholar 

  24. Singh L, Arokiasamy P, Singh PK, Rai RK. Determinants of gender differences in self-rated health among older population: evidence from India. SAGE Open. 2013;3(2):2158244013487914.

    Article  Google Scholar 

  25. Acevedo-Garcia D, Bates LM, Osypuk TL, McArdle N. The effect of immigrant generation and duration on self-rated health among US adults 2003–2007. Soc Sci Med. 2010;71(6):1161–72.

    Article  Google Scholar 

  26. Peres MA, et al. Self-rated health among adults in Southern Brazil. Rev Saude Publica. 2010;44:901–11.

    Article  Google Scholar 

  27. Onadja Y, Bignami S, Rossier C, Zunzunegui M-V. The components of self-rated health among adults in Ouagadougou, Burkina Faso. Popul Health Metr. 2013;11(1):1–12.

    Article  Google Scholar 

  28. Shetterly SM, Baxter J, Mason LD, Hamman RF. Self-rated health among Hispanic vs non-Hispanic white adults: the San Luis Valley Health and Aging Study. Am J Public Health. 1996;86(12):1798–801.

    CAS  Article  Google Scholar 

  29. Boardman JD. Self-rated health among US adolescents. J Adolesc Health. 2006;38(4):401–8.

    Article  Google Scholar 

  30. Borges CM, Campos ACV, Vargas AD, Ferreira EF, Kawachi I. Social capital and self-rated health among adolescents in Brazil: an exploratory study. BMC Res Notes. 2010;3(1):1–6.

    Article  Google Scholar 

  31. Joffer J, Jerdén L, Öhman A, Flacking R. Exploring self-rated health among adolescents: a think-aloud study. BMC Public Health. 2016;16(1):1–10.

    Article  Google Scholar 

  32. Potrebny T, Torsheim T, Due P, Välimaa R, Suominen S, Eriksson C. Trends in excellent self-rated health among adolescents: a comparative Nordic study. Nord Välfärdsforskning Nord Welf Res. 2019;4(2):67–76.

    Article  Google Scholar 

  33. Wang MP, Ho SY, Lo WS, Lai MK, Lam TH. Smoking is associated with poor self-rated health among adolescents in Hong Kong. Nicotine Tob Res. 2012;14(6):682–7.

    Article  Google Scholar 

  34. Mendoza FS, Fuentes-Afflick E. Latino children’s health and the family-community health promotion model. West J Med. 1999;170(2):85.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Asfaw A, Lamanna F, Klasen S. Gender gap in parents’ financing strategy for hospitalization of their children: evidence from India. Health Econ. 2010;19(3):265–79.

    Article  Google Scholar 

  36. Lundberg SJ. The division of labor by new parents: does child gender matter? SSRN J. 2005. https://doi.org/10.2139/ssrn.826445.

    Article  Google Scholar 

  37. Luthar SS, Latendresse SJ. Children of the affluent. Curr Dir Psychol Sci. 2005;14(1):49–53. https://doi.org/10.1111/j.0963-7214.2005.00333.x.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Meireles AL, Xavier CC, Proietti FA, Caiaffa WT. Influence of individual and socio-environmental factors on self-rated health in adolescents. Rev Bras Epidemiol. 2015;18:538–51. https://doi.org/10.1590/1980-5497201500030002.

    Article  PubMed  Google Scholar 

  39. Bernard C. Recognizing and addressing child neglect in affluent families. Child Fam Soc Work. 2019;24(2):340–7.

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to the study participants for providing the valuable data. Authors are also thankful to David Jean Simon, Paris 1 Pantheon Sorbonne University, for copyediting this manuscript.

Funding

Authors did not receive any funding to carry out this research.

Author information

Authors and Affiliations

Authors

Contributions

The concept was drafted by RP. RP contributed to the analysis design. DWB advised on the paper and assisted in paper conceptualization. RP contributed in the comprehensive writing of the article. DWB edited the manuscript. RP reviewed the manuscript. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Ratna Patel.

Ethics declarations

Ethics approval and consent to participate

This study is based on primary data collected by the first author herself. The ethical approval was granted by the Student Research Ethics Committee of the International Institute for Population Sciences, Mumbai, India. Furthermore, the signed consent to participate was taken from each of the respondent. Also, in case of the respondents being minor, the written informed consent was also taken from their mothers. Besides, the informed consent was also taken from the head/principal of the school in which the adolescent girls were studying at the time of the survey.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Structured schedule for adolescent girls.

Additional file 2.

Structured schedule for mothers.

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Patel, R., Bansod, D.W. How do responses vary between mothers and their daughters on measuring daughter’s self-rated health (SRH): a study among school-going adolescent girls in the primary setting of Varanasi, India. BMC Res Notes 15, 289 (2022). https://doi.org/10.1186/s13104-022-06174-1

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Keywords

  • Self-rated health
  • Opinion difference
  • Adolescents
  • India