Reproductive Health Morbidities, Body Mass Index and Body Fat percentage: A community-based cross-sectional study among the urban women of Bhubaneswar, Odisha, India

doi:10.21203/rs.3.rs-4866625/v1

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Reproductive Health Morbidities, Body Mass Index and Body Fat percentage: A community-based cross-sectional study among the urban women of Bhubaneswar, Odisha, India

https://doi.org/10.21203/rs.3.rs-4866625/v1

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Background: Reproductive morbidity includes various conditions affecting the reproductive tract or resulting from reproductive behavior. The relationship between BMI, body fat, and reproductive health is not well-understood, especially in India and Odisha.

Objectives: To determine the prevalence of reproductive health morbidities and explore associations between BMI, body fat percentage, and these morbidities among urban women in Bhubaneswar, Odisha, India.

Design: A community-based cross-sectional study.

Methods: The study involved 210 women aged 15-49 from Bhubaneswar's urban areas. Information was gathered on sociodemographics, gynecological and menstrual history, obstetric history, family history of morbidities, and personal history of diet, lifestyle, and physical activity. Anthropometric measurements were assessed. Outcomes were self-reported symptoms of reproductive health morbidities experienced within the past year, categorized into menstrual disorders, RTIs/STIs, and PCOS. The presence of one or more symptoms was considered an outcome.

Results: The mean age of participants was 30.49 ± 8.45 years, with 52.4% aged ≤30 years. Irregular menstruation was the most prevalent symptom (17.14%). Significant associations were observed between self-reported symptoms suggestive of menstrual disorders, RTI/STIs, PCOS, and BMI categories. Total body fat percentage and symptoms of menstrual disorders and PCOS were significantly associated with reproductive health morbidities. Among underweight women, 60% reported reproductive health morbidities. The logistic regression model indicated that the highest odds of experiencing reproductive morbidities were found among overweight women and those with high to very high body fat percentages.

Conclusion: The study demonstrated the greatest prevalence of reproductive health morbidities (RTI/STI, PCOS, menstrual symptoms) among women in the overweight and underweight BMI category and those with high to very high body fat percentages. A binomial logistic regression analysis highlights the influence of age, employment status, regular exercise, and BMI classifications on reproductive health morbidities within the study population, urban women of Bhubaneswar. These associations were statistically significant, emphasizing the need to address BMI and body fat percentage in reproductive health initiatives in Bhubaneswar's urban areas, Odisha, India.

Reproductive Health Morbidities

Body Mass Index

Body Fat percentage

Women’s health

Reproductive health is a crucial component of overall health and well-being, particularly for women of reproductive age. Despite considerable progress in recent years, reproductive health morbidities continue to be a significant public health issue in many parts of the world, especially in developing countries like India (1). These morbidities encompass a range of conditions, including menstrual disorders, reproductive tract infections (RTIs), sexually transmitted infections (STIs), and polycystic ovary syndrome (PCOS), which can have long-lasting impacts on women's physical, emotional, and social well-being (2). Moreover, reproductive health morbidities are often underreported and inadequately addressed due to cultural, social, and economic barriers (3).

Urban India, characterized by rapid urbanization, socio-cultural diversity, and varying levels of socioeconomic development, presents a unique context for examining reproductive health morbidities. Previous studies have highlighted the role of various factors such as age, socioeconomic status, education, and occupation in shaping reproductive health outcomes in the Indian population (4). However, there is limited research focusing specifically on urban settings, where the interplay of these factors may differ significantly from rural contexts (5).

Emerging evidence suggests that body mass index (BMI) and body fat percentage may play a significant role in influencing reproductive health outcomes (6). Obesity, in particular, has been associated with a higher risk of menstrual irregularities, PCOS, and infertility (7). Conversely, underweight women may also face an increased risk of reproductive health morbidities due to the adverse effects of low body weight on hormonal regulation (8). Despite these associations, the relationship between BMI, body fat percentage, and reproductive health morbidities in the context of urban India remains poorly understood.

This study aims to address this knowledge gap by examining the prevalence of reproductive health morbidities and their association with BMI and body fat percentage among women of reproductive age in urban India.

. The associations between Body Mass Index (BMI), body fat, and reproductive health morbidities require further investigation in India, particularly in Odisha. Understanding these associations in this understudied population is clinically important to develop targeted interventions for improved reproductive health outcomes. This study focuses on the urban women of Bhubaneswar, a population with unique cultural and socio-demographic factors, in order to address the existing gaps.

Objectives

This study was undertaken with the following objectives:

To estimate the prevalence of reproductive health morbidities among the reproductive age group women using self-reported symptoms.

To find any association between Body Mass Index, Body fat percentage and reproductive health morbidities among them.

Place & Field of the study

The study has been carried out in the department of Community Medicine, Kalinga Institute of Medical Sciences (KIMS), Bhubaneswar, Odisha.

Study design

It was a community-based cross-sectional study involving a population of Women of reproductive age group (15-49yrs) residing in Urban areas of Bhubaneswar consisted of the study population.

All the Pregnant and lactating women, or those who had attained menopause (natural and surgical), who were either mentally ill or suffering from chronic debilitating illness, and those who migrated from the study area in between study period were excluded from the study.

Duration/time period of the study

Two years (October 2015–2017)

Sample size

In this study, we aimed to estimate the sample size based on the prevalence of any one symptom of reproductive health problems in India. We used a previously reported prevalence of 39.2% from a study by Sogarwal R. et al. (5). The sample size estimation was carried out as follows:

n = (Z^2 * p * q) / d^2, where:

n = required sample size
Z = Z-score corresponding to the desired confidence level (1.96 for a 95% confidence interval)
p = estimated prevalence of any one symptom of reproductive health problems in India (39.2% or 0.392)
q = 1 - p
d = allowable error (10% or 0.1)

Using this formula, the sample size was calculated:

n = (1.96^2 * 0.392 * 0.608) / 0.1^2 n ≈ 96.12

Considering a design effect (DEEF) of 2, the sample size was adjusted:

n = 96.12 * 2 n ≈ 192.24

To account for a non-response rate of 10%, we added 10% to the calculated sample size:

n = 192.24 + (0.1 * 192.24) n ≈ 211.47, Rounding up to a multiple of 10, the final sample size for this study was set at 210 participants

Sampling technique

Multi-stage cluster sampling technique was used, which included simple random sampling to identify the zones. The urban development area of this city consists of the Bhubaneswar Municipal Corporation (BMC)(1), which divides the city of Bhubaneswar into 3 geographical zones -North Zone,South-West Zone, South- East Zone. Out of the three zones, two zones- North zone and South-west zone, (i.e. 66.66% of the area under study) were selected by simple random sampling (Lottery method). In the next stage, simple Random sampling was used to identify the clusters (wards). A total of 21 wards were selected (11 from north zone and 10 from south-west zone). Next, Simple random sampling was used to identify first household and Kish Grid Technique (6) was used for selection of study participants within each identified household, one eligible study subject (reproductive age group woman) was selected, from each selected household. if more than one eligible study subject was found to be present in any of the households. More details of Kish Grid Technique has been provided in Additional file 1.

Study Tool and Data Collection

A predesigned, pretested, semi-structured interview schedule was used to collected data. The interview schedule was formulated by thorough literature search and detailed discussion among all investigators. For validation, the questionnaire was pre-tested on a subset of 25 individuals, which is approximately 12% of the overall study sample, to confirm its clarity, relevance, and suitability. Subsequently, a pilot study was executed to ascertain the schedule's practicality. Following the pretest, essential refinements were made to the tool."It was developed in English, translated into the local language (i.e. Odia) by a language consultant using clear and simple words and then back translated into English by another expert both unrelated to the study. Though the questionnaire had not been previously validated and published. However, it has been pre-tested and developed in consultation with experts in the field to ensure its appropriateness for the study population and objectives. (Additional file 3)

Data was obtained on the Socio-demograp0ic details, gynaecological history, menstrual history, present and past obstetric history, family history of relevant morbidities, personal history on diet, lifestyle and physical activities. Relevant self-reported symptoms related to reproductive health morbidities (experienced within the past 1 year) and their health seeking behavior was noted A general examination of the participant was done and their anthropometric measurements including height, weight, body mass index (BMI), Body fat percentage, Visceral fat percentage was also noted. Each measurement was done twice and the average of the two readings was recorded. A digital portable instrument (Omron Karada Scan Body Composition Monitor ^TM ) that works by the principle of Biolectrical Impedence Analysis (BIA) was used to analyse the body composition- Body Fat Percentage (%),Visceral fat etc. The Body Sensing Technology Karada Scan Body Composition Monitor – HBF-375 is used in our study to measure various aspects of body composition, such as weight, body fat percentage, visceral fat level, subcutaneous fat and skeletal muscle percentage, resting metabolism, BMI, and body age. The device utilizes bioelectrical impedance analysis (BIA) to obtain these measurements.The principle behind BIA involves attaching two or more conductors to a person's body and sending a small electric current through it. The resistance between the conductors provides a measure of body fat, as the resistance to electricity varies among adipose, muscular, and skeletal tissue

In our study, participants placed each bare foot on an electrode, allowing the current to flow up one leg, across the abdomen, and down the other leg. The accuracy of BIA measurements is dependent on controlling factors such as eating, drinking, and exercising, as hydration levels can significantly influence the flow of the electric current and thus the estimation of body fat. Therefore, we advised participants not to undergo measurements immediately after eating, drinking, exercising, or when dehydrated. The HBF-375 device requires inputting details such as sex and age, as these factors affect body fat distribution. The device uses formulae that take these factors into account, recognizing that men and women store fat differently in areas like the abdomen and thighs.

Since the BMI reported in the machine is based on non-Asian cut off, we calculated BMI based on the height and weight. Among the output values of the BIA machine, only visceral fat was used in the analysis of this study.

A team was made including the primary investigator with 2 Post Graduate students, Junior Residents, Interns and Health workers. A training session was conducted and all of them were trained and oriented about the purpose and method of the study. The team was trained as per the study tool by the investigator.

Total two visits were done by house to house survey by investigator and the team in the selected wards. In the first visit, identification of the study subject with local health worker was done. Each woman was explained about the purpose of the study and consent was taken and assured about the confidentiality at all level, so that the survey could be carried out smoothly. The second visit was utilized for data collection. The filling of schedule took around 30 minutes.

After filling the schedule, the researcher explained and provided health education about reproductive health and BMI to the women. They were explained about the various associated morbidities and how to prevent them and seek appropriate treatment. If any symptoms of reproductive health morbidity were found, the study subjects were decided to be counselled to visit gynaecologist for further check-up and confirmatory diagnosis.

Operational definitions

(Details in Additional file 2)

Reproductive age group women: According to World Health Organization, the age group of 15–49 years is termed as reproductive age group.(7)
Reproductive health morbidity: Reproductive morbidity includes any condition of the reproductive tract or any morbidity which is a consequence of reproductive behaviour.
Reproductive health morbidity “present”: Presence of one or more self-reported symptoms of reproductive health morbidity.
Self-reported symptoms suggestive of menstrual disorders (present): The symptoms of menstrual disorder considered were- irregular menstruation, oligomenorrhoea, polymenorrhoea, menorrhagia, dysmenorrhoea and mid-cycle bleeding or spotting.
Self-reported symptoms suggestive of RTIs / STIs (present) : The symptoms included- Excessive amount of vaginal discharge, abnormal coloured vaginal discharge, malodorous vaginal discharge, vulvo-vaginal itching, rashes and blisters in genitalia and genital ulcers.
Self-reported symptoms suggestive of PCOS (present) - Those who reported at least one symptom as mentioned under Rotterdam criteria were classified as symptomatic (8) and grouped under PCOS ‘present’.
Body mass index (BMI) was calculated by dividing the weight in kilogram by height in meter square. (9) Asian classification of obesity (10)(11) was used.

⮚ Body fat percentage and Visceral Fat:

Body fat percentage = [Body fat Mass (Kg) / Body weight (Kg) ] × 100,

Visceral Fat

For interpretation of cut-off values of body fat composition and visceral fat composition of the women, the following were considered (Table 0) (12)

Body Fat percentage %:	Cut off/Range	Visceral Fat Cut off/Range
Low	Up to 19.9	-
Normal	20 to 29.9	0.5 to 9.5
High	30 to 34.9	10.0 to 14.5
Very high	35 and more	15.0 to 30.0

Table-0 : Cut off values of body fat composition and visceral fat composition of the women

Statistical Analysis

Data was collected, coded, tabulated and analysed using EpiInfo version 7 software for statistical analysis and necessary statistical tests like chi square test and binary logistic regression were used. P value of less than 0.05 (P < 0.05) was considered significant.

In our study, we have considered potential confounding variables, including age, socioeconomic status, and other sociodemographic factors that may influence both BMI/Body fat percentage and reproductive health outcomes. We have reported age in the logistic regression analysis to control for their potential confounding effects and provide a more accurate estimation of the relationship between BMI, body fat percentage, and reproductive morbidities. By adjusting for confounders, we aim to minimize the risk of spurious associations and strengthen the validity of our findings

STROBE Guidelines were followed while preparing the manuscript.

A total of 210 women in the reproductive age-group were selected as study participants. The mean age of the study participants was 30.49 ± 8.45 years, with a range of 16–45 years, and the majority (52.4%) were aged 30 years or younger. Most participants (89%) were of Hindu religion, 72.9% were unemployed, and 14.3% were employed. A significant majority (85.2%) were married, and 52.9% belonged to the upper-lower socio-economic class (Table 1). Among the study subjects, the majority (62.9%) had a normal BMI. A total of 12.4% were classified as obese II, 9.5% as overweight, and 9% as underweight. The remaining 6.2% were categorized as obese I (Table 2). Both high and low levels of adiposity, whether measured by BMI or BF%, were linked to approximately a twofold increase in the risk of reproductive morbidities compared to individuals with normal adiposity levels. This risk magnitude closely resembled the nearly twofold higher risk of reproductive morbidities associated with using cloth instead of sanitary napkins for menstrual soakage and engaging in sedentary activity. However, the risks related to earlier menarche were found to be nearly half of those associated with adiposity levels.The prevalence of reproductive health morbidity (presence of at least one or more symptoms) was 39.5% (Table 1). About 60.5% did not report any reproductive health morbidity. Approximately 32% of participants reported 1–2 symptoms, and 7.6% reported more than 2 symptoms (Table 1). The mean number of symptoms present among the women was 0.7 ± 1.042.

Table 1

Distribution of the study participants according to Socio-demographic and other characteristics (N = 210)
Variables	Number of women (N = 210)	Percentage (%)
Age groups (in completed years)
≤ 30	110	52.4
31–40	63	30.0
41–49	37	17.6
Religion
Hindus	188	89.6
Muslims	16	7.6
Christians	3	1.4
Sikhs	3	1.4
Caste
General	168	80.0
Other Backward Classes	27	12.9
Scheduled Caste	8	3.8
Scheduled Tribes	7	3.3
Employment status
Unemployed	153	72.9
Students	27	12.8
Employed	30	14.3
Marital status
Married	179	85.2
Unmarried	21	10.0
Divorced / Separated / Widowed	10	4.8
Type of family
Nuclear family	183	87.1
Joint family	27	12.9
Level of education
Graduate and above	34	16.2
High School and intermediate	68	32.4
Middle School certificate	37	17.6
Primary School certificate	23	10.9
Illiterate	48	22.9
Socio-economic class (According to Modified Kuppuswamy Scale, 2016)
Upper middle class	4	1.9
Lower middle class	95	45.2
Upper lower class	111	52.9
BMI Categories
Underweight	20	9.5
Normal	132	62.9
Overweight	19	9.0
Obese I	13	6.2
Obese II	26	12.4
Body fat Percentage
Low	22	10.5
Normal	96	45.7
High	74	35.2
Very High	18	8.6
Visceral fat
Normal	84	40.0
High	118	56.2
Very High	8	3.8
Reproductive health morbidity
Present	83	39.5
Absent	127	60.5
Number of symptoms of reproductive health morbidity
0	127	60.5
1–2	67	31.9
> 2	16	7.6

Table 2 illustrates the association between sociodemographic characteristics, menstrual and obstetric history, contraceptive usage, lifestyle factors, and reproductive health morbidities. Age group, religion, employment status, parity, place of last delivery, age and menarche, type of menstrual soakage use, sedentary lifestyle, physical activity, tobacco use were significantly associated with the presence of reproductive morbidities.

Table 2

Association between sociodemographic characteristics, menstrual history obstetric history, contraceptive usage, lifestyle factors and reproductive health morbidities.
Variables	Reproductive health morbidity		Chi-square values and P values
Variables	Present	Absent	Chi-square values and P values
Age group (N = 210)
≤ 30 years (n = 110)	34 (30.9%)	76 (60.1%)	χ² =11.896, P = 0.003
31–40 years (n = 63)	36 (57.1%)	27 (42.9%)
41–49 years (n = 37)	13 (35.1%)	24 (64.9%)
Religion (N = 210)
Hindus (n = 188)	67 (35.6%)	121 (64.4%)	χ² =9.84, P = 0.002
Muslims, Christians and Sikhs (n = 22)	16 (72.7%)	6 (27.3%)	χ² =9.84, P = 0.002
Literacy (N = 210)
Literate (n = 162)	67 (41.4%)	95 (58.6%)	χ² =2.26, P = 0.1328
Illiterate (n = 48)	16 (33.3%)	32 (66.7%)	χ² =2.26, P = 0.1328
Employment status (N = 210)
Unemployed (n = 153)	67(43.8%)	86 (56.2%)	χ² =6.25, P = 0.04
Students (n = 27)	5 (18.5%)	22 (81.5%)
Employed (n = 30)	11 (36.7%)	19 (63.3%)
Socio-economic class (N = 210) (According to Modified Kuppuswamy Scale, 2016)
Middle class (n = 99)	42 (40.0%)	57 (60.0%)	χ² =0.45, P = 0.502
Upper Lower Class (n = 111)	41(36.9%)	70 (63.1%)	χ² =0.45, P = 0.502
Marital Status (N = 210)
Married (n = 179)	70 (39.1%)	109 (60.9%)	χ² =4.90, P = 0.783
Unmarried (n = 21)	8 (38.1%)	13 (61.9%)
Divorced / Separated / Widowed (n = 10)	5 (50.0%)	5 (50%)
Age at marriage (N = 189)
< 18 years (n = 16)	10 (62.5%)	6 (37.5%)	χ² =2.83, P = 0.0925
> 18years (n = 173)	65 (37.6%)	108 (62.4%)	χ² =2.83, P = 0.0925
Contraceptive usage (ever married women) (N = 189)
None (n = 20)	8 (40.0%)	12 (60.0%)	χ² =46.82, P < 0.0001
Oral contraceptive pills (n = 16)	7 (43.8%)	9 (56.2%)
Copper T (n = 35)	26 (74.3%)	9 (25.7%)
Condom (n = 90)	15 (16.7%)	75 (83.3%)
Permanent sterilization (n = 28)	19 (67.9%)	9 (32.1%)
Parity (N = 210)
0 (n = 36)	14 (38.9%)	22 (61.1%)	χ² =20.46, P < 0.0001
1–2 (n = 111)	30 (27.0%)	81 (73.0%)
≥ 3 (n = 63)	39 (61.9%)	24 (38.1%)
Place of last delivery (N = 174)
Home delivery (n = 58)	32 (55.2%)	26 (44.8%)	χ² = 7.81, P = 0.005
Institutional delivery (n = 116)	37 (31.9%)	79 (68.1%)	χ² = 7.81, P = 0.005
History of abortion (N = 189)
Yes (n = 33)	18 (54.5%)	15 (45.5%)	χ² = 2.98, P = 0.08
No (n = 156)	57 (36.5%)	99 (63.5%)	χ² = 2.98, P = 0.08
Age at menarche (N = 210)
≤ 12 years (n = 126)	72 (57.1%)	54 (52.9%)	χ² =39.09, P < 0.0001
≥ 13 years (n = 84)	11 (13.1%)	73 (86.9%)	χ² =39.09, P < 0.0001
Type of menstrual soakage used (N = 210)
Sanitary Napkin or Pad (n = 157)	47 (29.9%)	110 (70.1%)	χ² =22.36, P < 0.0001
Cloth (n = 53)	36 (67.9%)	17 (32.1%)	χ² =22.36, P < 0.0001
Tobacco use (smoke or smokeless tobacco) (N = 210)
Yes (n = 24)	19 (79.2%)	5 (20.8%)	χ² =15.99, P < 0.0001
No (n = 186)	64 (34.4%)	122 (65.6%)	χ² =15.99, P < 0.0001
Alcohol use (N = 210)
Yes (n = 17)	8 (47.1%)	9 (52.9%)	χ² =0.16, P = 0.6892
No (n = 193)	75 (38.9%)	118 (61.1%)	χ² =0.16, P = 0.6892
Regular Exercise(N = 210)
Yes (n = 95)	45 (47.4%)	50 (52.6%)	χ² =3.83, P < 0.05
No (n = 115)	38 (33.0%)	77 (67.0%)	χ² =3.83, P < 0.05
Sedentary lifestyle (N = 210)
Yes (n = 52)	34 (65.4%)	18 (34.6%)	χ² =19.338, P < 0.0001
No (n = 158)	49 (31.0%)	109 (69.0%)	χ² =19.338, P < 0.0001

Irregular menstruation was the most common self-reported symptom (17.14%). Dysmenorrhea (12.38%) was the second most common, followed by excessive vaginal discharge (10.96%). Vulvo-vaginal itching and acne were reported by 8.1% of the study subjects. Genital ulcers, inguinal swelling, and post-coital bleeding were not reported by any participants (Fig. 1).

Overall, 33.8% of women reported symptoms suggestive of menstrual disorders, followed by 17.6% reporting symptoms suggestive of RTIs/STIs, and 15.3% with symptoms indicative of PCOS. The highest prevalence of reproductive health morbidity (63.2%) was reported by women in the overweight BMI category. Obese II (61.5%) and obese I (53.8%) women also reported high rates of reproductive health morbidity. Interestingly, 60% of underweight women reported reproductive health morbidity. The association between reproductive health morbidities and BMI categories was highly statistically significant (P < 0.0001). Table 3shows distribution of study subjects according to presence of reproductive health morbidity, BMI status, Body Fat percentage and visceral fat.

A statistically significant association (P < 0.0001) was found between self-reported symptoms suggestive of menstrual disorders, RTI/STIs, and PCOS and BMI categories (Table 4). Variables such as age group, religion, contraceptive usage, parity, place of last delivery, age at menarche, type of menstrual soakage, tobacco use, and sedentary lifestyle were all highly significantly associated (P < 0.001) with reproductive health morbidities. Employment status and regular exercise were also significantly associated (P < 0.05) with the presence of reproductive health morbidities. In contrast, literacy, socio-economic class, marital status, age at marriage, history of abortion, and alcohol use were not found to be significantly associated with reproductive health morbidities in our study population.

Table 4

Distribution of study subjects according to BMI categories and symptoms of specific reproductive health morbidities (N = 210)
BMI categories*	Self-reported symptoms suggestive of menstrual disorders
BMI categories*	Present (n = 71)	Absent (n = 139)
Underweight (n = 20)	12 (60.0%)	8 (40.0%)	P < 0.0001
Normal (n = 132)	27 (20.5%)	105 (79.5%)
Overweight / Obese I/ Obese II (n = 58)	32 (55.2%)	26 (44.8%)
BMI categories*	Self-reported symptoms suggestive of RTIs / STIs
BMI categories*	Present (n = 37)	Absent (n = 173)	P < 0.0001
Underweight (n = 20)	6 (30.0%)	14 (70.0%)
Normal (n = 132)	10 (7.6%)	122 (92.4%)
Overweight / Obese I/ Obese II (n = 58)	21 (36.2%)	37 (63.8%)
BMI categories*	Self-reported symptoms suggestive of PCOS
BMI categories*	Present (n = 32)	Absent (178)
Underweight (n = 20)	5 (25.0%)	15 (75.0%)	P < 0.0001
Normal (n = 132)	9 (6.8%)	123 (93.2%)
Overweight / Obese I/ Obese II (n = 58)	18 (31.0%)	40 (69.0%)

Table 5 shows the distribution of study subjects according to total body fat percentage and symptoms of specific reproductive health morbidities.

Table 5

Distribution of study subjects according to Total body fat percentage and symptoms of specific reproductive health morbidities (N = 210)
Body fat Percentage	Self-reported symptoms suggestive of menstrual disorders
Body fat Percentage	Present (n = 71)	Absent (n = 139)
Low (n = 22)	7 (31.8%)	15 (68.2%)	p < 0.0001
Normal (n = 96)	11(11.4%)	85 (88.6%)
High (n = 74)	41(55.4%)	33(44.6%)
Very High (n = 18)	12(66.6%)	6 (33.4%)
Body fat Percentage	Self-reported symptoms suggestive of RTIs / STIs
Body fat Percentage	Present (n = 37)	Absent (n = 173)
Low (n = 22)	5 (22.7%)	17(77.3%)	p = 0.46
Normal (n = 96)	17(17.7%)	79(82.3%)
High (n = 74)	10(13.5%)	64(86.5%)
Very High (n = 18)	5(27.7%)	13(72.3%)
Body fat Percentage	Self-reported symptoms suggestive of PCOS
Body fat Percentage	Present (n = 32)	Absent (178)
Low (n = 22)	5(22.7%)	17(77.3%)	p = 0.0016
Normal (n = 96)	5(5.2%)	91(94.8%)
High (n = 74)	16(21.6%)	58(78.4%)
Very High (n = 18)	6(33.4%)	12(66.6%)

Table 6 presents a binomial logistic regression analysis highlighting the association between various factors and reproductive health morbidities. The model, with an R-squared value of 0.31, indicates that 31% of the variability in reproductive co-morbidities can be attributed to the covariates included in the regression analysis. A p-value of < 0.001 confirms the model's statistical significance and suggests a reliable fit. In a detailed analysis of the data, it was observed that women within the age group of 31 to 40 years demonstrated a 2.9 fold increase in the odds of reproductive health morbidities compared to those in the 0 to 30 age category, a finding that was found to be statistically significant. Addressing employment status, unemployed women presented with 5.2 times heightened odds of reproductive morbidities relative to their employed counterparts. Pertaining to physical activity, the data underscores that the absence of regular exercise was associated with a 2.8-fold increase in the odds of reproductive morbidities. With respect to BMI categorizations, pre-obese women showcased the most amplified odds at 13.6 when juxtaposed with women of normal BMI. This was succeeded by overweight women at an odds ratio of 7.8, obese women at 6.8, and underweight women at 6.6; each of these associations was statistically significant. Moreover, alcohol intake was associated with a 1.9-times the odds of reproductive morbidities relative to those who did not drink alcohol. Regarding religious denominations, Christian women exhibited odds of reproductive co-morbidities that were 4.1 times higher than those of Hindu women, though this was not statistically significant.

Table 6: A Binomial Logistic Regression analysis of reproductive morbidities and other factors

In summation, this analysis highlights the salient influence of determinants such as age, employment status, exercise habits, and BMI classifications on reproductive health morbidities within the study population, urban women of Bhubaneswar. .However, there's an evident need for more comprehensive research to find other potential influences.

In the present study, 62.9% of participants had a normal BMI, while 12.4% were classified as obese II, 9.5% as overweight, 9% as underweight, and 6.2% as the remaining women. We found that 39.5% of participants experienced reproductive health morbidity, defined by at least one symptom. A majority (32%) reported 1–2 symptoms, while 7.6% experienced more than two. Irregular menstruation was the most common symptom, reported by 17.14% of women. This aligns with a study by Sidhu et al. (13), who found a prevalence of 20% for overweight and 25.3% for obesity in 1,000 urban adult females in Punjab.

A highly significant association (P < 0.0001) was found between self-reported symptoms of menstrual problems, RTI/STIs, PCOS, and BMI categories using the chi-square test. Age group, religion, menstrual soakage type, parity, location of the most recent delivery, age at menarche, tobacco use, and sedentary lifestyle were also significantly associated (P < 0.001) with reproductive health morbidities. employment status and frequent exercise showed a significant (P < 0.05) association with reproductive health morbidities, but no significant relationship was found with literacy, socioeconomic level, marital status, age at marriage, history of abortion, or alcohol consumption.

Wei et al. (14) found that 26% of women with a BMI ≥ 30 had irregular menstrual cycles, compared to 14% of those with a BMI of 20–24. Sheela et al. (15) reported that 28.67% of patients with a normal BMI had irregular periods, while 69% of obese women with BMIs > 30 kg/m and 65% of those with BMIs between 25-29.99 kg/m had irregular cycles. Other studies have found that 30–47% of obese women experience irregular menstrual periods (16, 17).

In our study, around 31% of overweight/obese I/obese II participants and 25% of underweight participants reported PCOS symptoms, compared to just 6.8% in the normal BMI category. This association was statistically significant. However, a study by Gill et al. (18) in North India found no significant association between BMI status and PCOS, possibly due to the younger age range of participants (18–25 years).

Our study found a 17.61% prevalence of RTI/STI symptoms among the 210 participants. Among overweight/pre-obese/obese participants, 36.2% reported RTI/STI symptoms, while 30% of underweight participants did. A highly significant association (P < 0.0001) was found between self-reported RTI/STI symptoms and BMI categories. Agarwal et al. (19) found a significant association between itching and irritation around the vagina and obesity in adult women in Delhi, while Ventolini et al. (20) reported a significant association between vulvovaginal infection and obesity in a Texas gynecology clinic. In our study, high and very high body fat percentages were associated with symptoms suggestive of menstrual disorders, similar to findings by Wei et al. (14). Koskova et al. (21) found a significant increase in fat percentage among fully reproductive and menopausal women but not postmenopausal women, aligning with our findings of a maximum prevalence (57.1%) of reproductive health morbidity among women aged 31–40.

Kawwass et al. (2016) found that the odds of intrauterine pregnancy were considerably lower in both underweight and obese women compared to women with a normal BMI (odds ratio: 0.97 and 0.94, respectively) (22). Our study found similar results, with higher reproductive morbidities in obese-1 (odds ratio: 4.5) and underweight (odds ratio: 4.3) women. This observation is supported by various studies that have found obese women are more likely to experience miscarriage, congenital fetal deformity, spontaneous preterm birth, and stillbirth (23)and that obesity is associated with pregnancy loss in reproductive-age women and those receiving assisted reproduction procedures as reported by Fedorcsák et al. (24), Lashen et al., (25). Regarding body fat, our study found that reproductive morbidity chances were higher in those with low body fat (odds ratio: 1.57). This is supported by a study by Frisch (1991), where evidence suggests that a high amount of body fat (26%-28%) in older women is required for normal ovulatory cycles and may influence reproductive ability directly (26) A comprehensive understanding of the prevalence, factors, and practices associated with reproductive health morbidities in the urban Indian population is crucial for developing effective public health interventions and policies. Furthermore, sedentary lifestyles have been associated with a higher risk of various health conditions, including reproductive health issues. Investigating the link between sedentary activity and reproductive health morbidities can inform interventions to promote healthy lifestyles and improve women's health. Lastly, menstrual hygiene and the choice of menstrual soakage materials can have a considerable impact on women's reproductive health. A better understanding of the relationship between menstrual soakage and reproductive health morbidities can guide efforts to improve menstrual hygiene education and access to safe and affordable menstrual products.

By broadening the focus of our study to include these factors further and add a more elaborate analysis process with all the interplaying factors, we can provide a more comprehensive perspective on the complex interplay of factors influencing reproductive health in the urban Indian population. This approach would allow public health practitioners and policymakers to design more targeted and effective interventions to address the multifaceted nature of reproductive health issues and promote overall well-being for women in urban India.

The socio-cultural uniqueness of our study population adds another layer of complexity to the understanding of reproductive health morbidities in urban Bhubaneswar, India. Our study participants come from diverse religious, ethnic, and often linguistic backgrounds, reflecting the rich cultural mosaic of Indian society. This diversity presents unique challenges and opportunities for public health interventions, as different socio-cultural contexts may influence women's knowledge, attitudes, and practices related to reproductive health. For example, cultural beliefs and norms around menstruation, sexual health, and family planning may vary significantly across different communities, potentially affecting the prevalence and management of reproductive health morbidities. Furthermore, socio-cultural factors such as gender norms, women's empowerment, and access to education and healthcare services can also play a significant role in shaping women's reproductive health outcomes. By acknowledging and incorporating this socio-cultural uniqueness into our study, we can better tailor interventions and policies to meet the specific needs of the diverse urban Indian population, ensuring that all women have access to the resources and support they need to achieve optimal reproductive health.

Limitations

We recognize that our study has several constraints that should be considered when interpreting the results:

Cross-sectional design: The nature of this cross-sectional study limits its usage in finding a temporal association between cause and effect. This design only provides a snapshot of the relationship between BMI, body fat percentage, and reproductive health morbidities at one point in time, which prevents us from drawing conclusions about causality.

Self-reported symptoms: The prevalence of reproductive health morbidity was based on self-reported symptoms, which might be subject to reporting bias. Women might underreport or overreport their symptoms due to various reasons such as fear of stigmatization, lack of knowledge, or misunderstanding of the questions.

Recall bias: Since retrospective information was collected from the study participants with a time span of 1 year, there is a potential for recall bias, where women might not accurately remember the details of their symptoms, leading to misclassification of their health status.

Social desirability bias: This is a potential limitation where women might report more socially acceptable responses than their actual degree of severity. The sensitive nature of reproductive health issues might lead women to provide responses that they perceive to be more socially acceptable, which could skew the results.

Lack of clinical validation: Our study relied on self-reported symptoms rather than clinically validated diagnoses. This might lead to underestimation or overestimation of the prevalence of reproductive health morbidities and their association with BMI and body fat percentage.

Confounding factors: Our study might not have controlled for all possible confounding factors that could influence the observed associations, such as underlying medical conditions, hormonal imbalances, or stress levels.

Generalizability: The findings of our study might not be generalizable to other populations with different sociodemographic characteristics, lifestyles, or healthcare systems.

It is important to note that some of the symptoms assessed in our study, particularly those suggestive of PCOS, can be quite non-specific and may be indicative of other conditions. The presence of these symptoms does not necessarily confirm the diagnosis of PCOS or other reproductive health morbidities.

Despite the limitations, we must acknowledge that the strengths of our study lie in its methodological rigor, the representativeness of the study population, and its comprehensive examination of reproductive health morbidities in the context of urban Bhubaneswar, Odisha India. By acknowledging the unique socio-cultural aspects of our study participants and incorporating a wide range of factors that may influence reproductive health, our study provides valuable insights for public health practitioners and policymakers working to improve reproductive health outcomes for women in urban India.

Recommendations

Education and awareness: One of the key strategies to tackle reproductive health morbidities is to raise awareness among women about the symptoms and potential risk factors, such as BMI, that can contribute to these health issues. By empowering women with knowledge, they can recognize early symptoms, seek timely medical intervention, and improve their overall quality of life. Targeted information, education, and communication (IEC) activities should be implemented in the community to disseminate relevant and accurate information.

Symptom-based intervention approach: To ensure early detection and prompt medical attention, a symptom-based intervention approach should be integrated into community settings. This involves providing training and orientation to community health providers to effectively recognize and address reproductive health symptoms. By strengthening the capacity of healthcare providers at the community level, women can receive timely and appropriate care.

Strengthening existing programs: Government-initiated Reproductive and Child Health (RCH) programs should be strengthened through micro-monitoring to closely track health outcomes at the community level. Effective implementation of these programs can be ensured by providing comprehensive training and support to community health providers, thereby reaching the intended beneficiaries and addressing their specific needs.

Promotive and preventive strategies: Targeted promotive and preventive strategies should be employed to promote healthy behaviors and lifestyles among women of reproductive age. Community-based IEC activities can emphasize the importance of maintaining a healthy weight, adopting a balanced diet, engaging in regular physical activity, and seeking timely care for reproductive health issues. By encouraging positive health practices, the risk of reproductive morbidities can be reduced.

Further research: Considering the significant health challenges faced by women, it is imperative to invest in further research to address their specific health needs. This includes gaining a deeper understanding of the complex interplay of factors contributing to reproductive health morbidities, evaluating the effectiveness of intervention strategies, and identifying areas for improvement in existing programs and policies. Evidence-based research is essential to inform and guide policy decisions and healthcare interventions.

The present study revealed a high prevalence of reproductive health morbidities and their significant association with abnormal BMI. These findings underscore the importance of addressing the rising prevalence of overweight and obesity among women of reproductive age to improve their reproductive health outcomes. Healthcare providers should be trained to identify and manage reproductive health morbidities in primary healthcare settings. Additionally, interventions targeting lifestyle modification and public health awareness campaigns could help women maintain a healthy weight and body fat percentage, thereby enhancing their reproductive health.

Ethics approval and consent to participate

The study has been approved by the Institute ethics committee of Kalinga Institute of Medical Sciences, Bhubaneswar, India (KIMS/KIIT/IEC/2015).

Written informed consent was obtained from all participants included in this study prior to data collection. The consent form provided information about the study, its objectives, potential risks and benefits, as well as the participant’s right to withdraw from the study at any time. Participants were also informed that the data collected from them would be used for research purposes and may be published in scientific journals or presented at conferences, but their identities would be kept confidential. Notably, while the eligible age group for women in the reproductive age category is 15-49 years, only those aged 18 years and above consented to participate in this study. Therefore, informed consent was directly obtained from the participants themselves.

Author contributions

Adrija Roy, Ipsa Mohapatra, Om Prakash Panigrahi: Conceptualization and Methodology, Software: Adrija Roy, Arun Mitra, Data curation :Adrija Roy, Arshad Ayub, Adrija Roy, Arun Mitra, Arshad Ayub- Original draft preparation. Adrija Roy, Om Prakash Panigrahi, Ipsa Mohapatra: Visualization, Investigation. Om Prakash Panigrahi, Ipsa Mohapatra: Supervision. All authors: Writing, Reviewing and Editing

Acknowledgments

We express our gratitude to staff and health-workers of Urban Health and Training Centre, Kalinga Institute of Medical Sciences, Bhubaneswar; for their help in data collection and interview, of the study participants; to the post graduate students and interns who assisted during the data collection, and finally, the study participants.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors and was entirely self-funded.

Conflict of Interest

The authors declare that they have no competing interests.

Availability of data and materials

The anonymized datasets are available upon reasonable request, as they contain sensitive information shared by the study participants. Detailed insights into the study methodology and results can also be found in the Supplementary articles . It is of utmost importance to us to protect the privacy and confidentiality of our study participants while promoting transparency in our research process."

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No competing interests reported.

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Reproductive Health Morbidities, Body Mass Index and Body Fat percentage: A community-based cross-sectional study among the urban women of Bhubaneswar, Odisha, India

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