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Full Research Article

Study on the Risk Factors Analysis for Subclinical Endometritis in Cohort of Repeat Breeder Indigenous Cattle

A
Ashutosh Tripathi1
https://orcid.org/0000-0002-8511-9099
M
Manish Kumar Shukla1
https://orcid.org/0000-0002-5443-1493
A
Atul Kumar Verma1
https://orcid.org/0009-0007-6156-6525
N
Neelesh Kapoor2
https://orcid.org/0000-0002-2201-3465
V
Vijay Singh1
https://orcid.org/0009-0007-3842-1854
A
Arbind Singh3,*
https://orcid.org/0009-0002-8821-5032
R
Rajbir Singh4
https://orcid.org/0009-0004-1418-0580
1Department of Veterinary Gynaecology and Obstetrics, College of Veterinary and Animal Sciences, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
2Department of DNA Fingerprinting, College of Biotechnology, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
3Department of Livestock Farm Complex, College of Veterinary and Animal Sciences, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
4Department of Animal Husbandry, School of Agricultural Science, IIMT University, Meerut-250 001, Uttar Pradesh, India.

ABSTRACT

Background: The subclinical endometritis (SCE) has severe negative impacts over the fertility in bovines. There are several risk factors identified which predispose animals to SCE. The model development to evaluate the risk factors and population level impact in cohort of indigenous repeat breeder cows may be helpful for identification of cows at high risk of developing SCE.

Methods: A total of 90 indigenous cattle with the history of repeat breeding were screened for subclinical endometritis (SCE) through endometrial cytology technique. The 5% cut off value of PMNs was established as threshold for SCE. Events likely to predispose SCE were taken in to consideration to perform a risk factor analysis using univariate logistic regression and multiple logistic regression model building. The population attributable fraction was calculated to chalk out the final risk factors among all.

Result: The baseline disease prevalence established was 32.22% (29/ 90). Univariate analysis and multivariable logistic regression identified parity and retained fetal membranes (RFM) as the only independent risk factors to SCE. Multiparous status increased the odds of subclinical endometritis 7.75-fold (OR=7.75; 95% CI: 1.81-33.33; RR=2.75). RFM triggered even severe risk inflation (OR=15.87; 95% CI: 2.99-83.33; RR=4.60). The population attributable fraction (PAF) metrics showed that multiparity caused 48.3% of all cases, while RFM accounted for 43.2% of the cases. The multivariable framework demonstrated strong predictive validation with area under the curve (AUC) of 0.794. The study concluded that RFM is the more deadly pathology, therefore wide preventative strategies targeting the multiparous cows may yield the highest herd-level reduction in disease burden. This model may offer an accurate, cost-effective and even early warning filter for farm managers and the farmers.

INTRODUCTION

Subclinical endometritis (SCE), is the endometrial inflammation without overt clinical signs (Sheldon et al., 2019). Inadequate detection and management of SCE lead suboptimal conception rates and prolonging the calving intervals (Barajas et al., 2018). The prevalence reported for SCE is high (Sheldon et al., 2019), with negative impacts on reproductive performance (Gilbert et al., 2005; Barajas et al., 2018). The physio-pathological mechanism includes chronic inflammatory response of endometrium to long term persistent bacterial infections. The risk factors/etiology of SCE are still under debate that may include unspecific infection in the uterus, or persistent inflammatory response of the endometrium to bacterial infections (Wagener et al., 2017). Many risk factors for SCE, like Negative energy balance (NEB), metabolic diseases, uterine infections during the puerperium period, calving assistance (CA) and RFM, have been identified previously in large-scale dairies (Dubuc et al., 2010; Cheong et al., 2011; Wagener et al., 2017; Yanez et al., 2022). Therefore, the present study is undertaken based on the novelty in the development of a multivariable predictive model for SCE in cattle, establishing multiple significant risk factors to estimate disease probability at the individual animal level. This approach provides a potential decision-support tool for early identification of affected animals and may complement existing diagnostic strategies for improving reproductive management in dairy herds.

MATERIALS AND METHODS

Selection of animals
 
Ninety repeat breeder (RB) indigenous cows (BCS 3 or more) from District Meerut and nearby areas were selected for the current research. Thereafter visual inspection of the discharge and rectal palpation were performed, as well as transrectal ultrasound to evaluate the reproductive tract (6.5 MHz linear-array transducer, Minitube portable USG). All these animals were screened for SCE through cytobrush technique (Wagener et al., 2017). All the animals under study were managed under optimum herd and nutritional managemental conditions.
 
Diagnosis of subclinical endometritis
 
The endometrial cytology was performed using modified cytobrush gun (Mayfair technologies, Ludhiana). The cytology gun fitted with brush was inserted aseptically into the uterine body, thereafter the plunger was pressed to externalize the cytobrush inside the uterine body, which was rotated gently against the uterine wall to collect an endometrial sample. Following collection of the sample, the cytobrush was retracted back in the rod to avoid contamination. Thereafter cytobrush was recovered and slide was prepared by rolling it on the glass slide and allowing it to air-dry. The slides were stained using field’s stain and then examined at 400X magnification. The cut off value of PMNs was ≥5% for declaring animal positive for SCE (Wagener et al., 2017).
 
Risk factors
 
The puerperium and post-partum events were recorded based on the history and observation and the events of interest were recorded to determine the possible risk factors for SCE. Five age group categories were included in the present study: 4-5, 5-6, 6-7, 7-8 and 8-9 years. Five calving seasons (CS) within the year were taken into consideration for statistical analysis: the rainy season, autumn, spring, summer and winter. Two parity categories were included: primiparous and multiparous. The assisted delivery, RFM, calf sex and abortion were also recorded in the current study. Whether the animals suffered with any kind of uterine diseases (UD) first 3 weeks post-partum or not was also included in the study.
 
Statistical analysis
 
In the present study descriptive statistics using SPSS were utilized to summarize the distribution of explanatory variables and the status of subclinical endometritis (SCE). The frequencies and percentages were calculated for all categorical variables. The association between individual explanatory variables and SCE was assessed using the chi-square test, while fisher’s exact test was applied when any cell’s expected frequency in any of the cell was <5. The variables that reveal significant (P≤0.35) association with SCE in univariate analysis were further considered for multivariable analysis. Therefore, in the first stage, univariate logistic regression was used to screen potential predictor variables using P≤0.35 as described by Villasenor-Gonzalez et al. (2025) and in the second stage, these variables were entered into a multivariable logistic regression model with backward elimination and a retention criterion of P<0.10. A relatively liberal screening threshold is commonly recommended in epidemiological and regression modeling studies to avoid excluding variables that may not appear significant in univariate analysis but may become important after adjustment for confounding in multivariable analysis. Under univariate binary logistic regression, crude odds ratios (OR) and their 95% confidence intervals (CI) were estimated. A backward stepwise elimination method was employed, retaining variables with P<0.05 in the final model. Adjusted odds ratios (OR) with 95% confidence intervals were derived from the final model. Relative risks (RR) were estimated from adjusted odds ratios using the approach described by Zhang and Yu (1998). The proportion of exposed animals (Pe) and the population attributable fraction (PAF) were calculated to evaluate the relative contribution of significant risk factors to the occurrence of SCE. Statistical significance was determined at P<0.05. The multicollinearity among the explanatory variables was evaluated using the Variance Inflation Factor (VIF) and no evidence of problematic multicollinearity was observed (all VIF values <5).

RESULTS AND DISCUSSION

Explanatory variables and cohort baseline epidemiology
 
The 5-6 years age group showed dominant footprint, accounting for 35.6% (n=32) of the cows. Parity classification including primiparous and multiparous was relatively balanced, comprising 46.7% (n=42) and 53.3% (n=48) of the herd, respectively. The highest incidences were recorded during the rainy season (26.7%, n=24), followed by spring (23.3%, n=21) and winter (21.1%, n=19). The majority of animals had calving without requiring assistance (82.2%, n=74) and 96.7% (n=87) of the animals had no abortion history. The RFM and post-partum uterine diseases were diagnosed in 21.1% (n=19) and 22.2% (n=20) of the animals, respectively. The male and female calves comprised of 53.3% (n=48) and 46.7% (n=42) of the total births. The prevalence of SCE was established at 32.2% (n=29), leaving a baseline of 67.8% (n=61) consisting of negative cases (Table 1).

Table 1: Distribution of explanatory variables and outcome status.


 
Univariate screenings and predictive associations
 
Univariate relationship screening through chi-square and fisher’s exact tests established highly significant associations (p<0.05) among SCE and primary explanatory variables, excluding the abortion history (p=0.242) and sex of the calf (p=0.809) (Table 2). Fisher’s exact test was rigorously substituted across parameters where low cell frequencies violated standard chi-square distributional assumptions (Age group and abortion history).

Table 2: Association of explanatory variables with subclinical endometritis.


       
To isolate localized directional effects, individual variables were evaluated using univariate binary logistic regression (Table 3). Parity (p=0.004), calving season (p=0.099), calving assistance (p<0.001), abortion history (p=0.231), RFM (p<0.001) and uterine disease (p<0.001) exhibited strong regression footprints. Applying an expansion threshold constraint of (p≤0.35) to capture weak or confounding interactions, parity, calving assistance, abortion history, RFM and uterine disease were cleared for multivariable entry (Villasenor-Gonzalez et al., 2025) through backward stepwise selection method.

Table 3: Univariate logistic regression analysis of factors associated with subclinical endometritis.



Multivariable logistic regression and model diagnostics
 
The independent risk configuration was mapped using multivariable logistic regression utilizing a backward stepwise selection sequence. Independent variables with non-significant structural impact were iteratively stripped, whereas variables with true predictive strength (p<0.05) were retained in the final framework.
       
The final model identified parity and RFM as the independent predictors of SCE. Model diagnostic tests showed that removing parity from the equation significantly compromised the overall model fit (Change in -2 Log Likelihood=9.166; p=0.01). Similarly, the removal of RFM caused an even more drastic collapse in model performance (Change in -2 Log Likelihood=29.047; p<0.01), proving that both risk parameters are independently and highly significantly (p<0.01) tied to SCE development.
       
The definitive effect points and precision estimates are mentioned in Table 4. Multiparous animals demonstrated a 7.75-fold inflation in the odds of developing subclinical endometritis relative to primiparous animals (OR=7.75; 95% CI: 1.81-33.33). Furthermore, the presence of RFM generated a profound impact, with affected animals exhibiting a 15.87-fold increase in disease odds compared to their non-retained counterparts (OR=15.87); 95% CI: 2.99-83.33).

Table 4: Multivariable logistic regression model for risk factors associated with subclinical endometritis.


       
The corresponding true epidemiological risks showed a Relative Risk (RR) of 2.75 (95% CI: 1.31-5.78) for multiparity and an RR of 4.60 (95% CI: 2.71-7.80) for RFM. The PAF for multiparity and RFM stood at 48.3% and 43.2%, respectively (Fig 1). Predictive performance assessment yielded an area under the curve (AUC) of 0.794, demonstrating a strong, internally validated diagnostic fit (Fig 2).

Fig 1: Population attributable factor (Multivariable logistic regression) for risk factors associated with SCE.



Fig 2: Receiver operating characteristic (ROC) curve validating the multivariable diagnostic risk model.


       
Final analysis through multivariable logistic regression established both parity and RFM as significant risk factors for developing SCE (Fig 3). Multiparous cows had 7.75 times higher odds of developing SCE compared to primiparous (OR=7.75; 95% CI: 1.81-33.33). Similar to this, animals affected with RFM exhibited 15.87 times higher odds of SCE than those not (OR=15.87; 95% CI: 2.99-83.33).

Fig 3: Multivariable logistic regression model for risk factors associated with SCE.


       
The chances of encountering SCE were found 2.75 times higher among multiparous cows (RR=2.75; 95% CI: 1.31-5.78) and it was 4.60 times higher in animals affected with RFM (RR=4.60; 95% CI: 2.71-7.80). The PAF indicated that out of total, 48.3% and 43.2% of SCE cases could be attributed to the multiparity and RFM, respectively. Also, high PAF for these both conditions (aiming to reduce or treat) suggest need for preventive strategies against these two risk factors so as to deal with SCE.
       
Several herd-level risk factors to SCE like CS, RFM, UD, parity, metabolic diseases like negative energy balance, calving assistance, calf sex and timing of sampling after parturition have been identified in previous studies (Kasimanickam et al., 2004; Dubuc et al., 2010; Cheong et al., 2011; Prunner et al., 2014; Yanez et al., 2022; Villasenor-Gonzalez et al., 2025) with the prevalence reported ranging from 7 to 53% between 3rd to 7th week post-partum (Quintela et al., 2018; Villasenor-Gonzalez et al., 2025). Among routine screened animals, prevalence reported was 6.35% (Narwade et al., 2026), while among repeat breeder cows, the prevalence reported is between 12.7 to 52.7% (Salasel et al., 2010; Janowski et al., 2013; Pothmann et al., 2015; Pascottini et al., 2017; Wagener et al., 2017; Bedewy and Rahawy, 2019; Emre, 2024; Pande et al., 2025; Villar et al., 2025).
       
Risk factors analysis in a previous study for SCE in RB cows including, season, parity, reproductive pathologies, no variable revealed significant effect, however in the present study the parity and RFM showed significant footprints. However similar to previous study numerical difference in case of season and reproductive pathology were noticed in present study too (Villar et al., 2025). However, in other studies over effect of reproductive pathologies, statistically higher incidence of SCE was noticed among animals suffering with post-partum reproductive pathologies (Salasel et al., 2010). The effect of parity in occurrence of SCE has also been observed in previous studies similar to the present study. Also, in those studies during first artificial insemination the month/season of AI also seem to affect significantly the occurrence of SCE (Pascottini et al., 2017; Diaz-Lundahl et al., 2021) however in the present study season during calving does not seem to affect occurrence of SCE similar to previous study (Prunner et al., 2014) however another study differs with this opinion (Villasenor-Gonzalez et al., 2025). In previous studies uterine infections and CA were identified as risk factors (Dubuc et al., 2010; Cheong et al., 2011; Villasenor-Gonzalez et al., 2025) but the present study CA didn’t seem to affect occurrence of SCE. RFM may be a sequence from CA and obstetrical procedures (Montiel-Olguin et al., 2019). RFM, altering the immunity status (Chebel, 2021) may be the reason for it being the predisposing factor for SCE. The multiparity in cows lead to increased uterine trauma and slower involution following repeated calvings. Also, reduced postpartum immune competence, higher post-partum disorders (e.g. RFM), greater metabolic stress and impaired bacterial clearance may potentially predispose multiparous cows to SCE (Kasimanickam et al., 2004; Sheldon et al., 2006; LeBlanc, 2008; Sheldon et al., 2009; LeBlanc, 2012).
       
The indigenous cattle are often raised by marginal/ smallholders, where routine postpartum reproductive examinations are often impractical because of limited veterinary access and financial constraints, the established risk factors may be used to prioritize the animals at risk for timely examination and proper intervention. This model/ targeted strategy may enable efficient use of veterinary services, may reduce unnecessary diagnostic costs and may support reproductive efficiency and farm profitability.

LIMITATIONS

The limitation of the present study includes relatively small sample size (n = 90), which may have limited the statistical analytical power and the precision of the estimated variable associations i.e. wide confidence interval. Also, the animals were based out of a single geographical region, which may have limited the generalizability of the findings to other geographical conditions. The indigenous cattle were included in the present study, therefore the established risk factors may not directly applicable to other crossbred or exotic breeds. The cross-sectional observational design allows identification of associations but does not permit causal inferences. It is recommended that future studies involving larger sample size, multi-regional sampling comprising different breeds and longitudinal study designs are warranted to further validate and extend the current findings.

CONCLUSION

There is significant prevalence of SCE among indigenous repeat breeder cattle. Preliminary univariate analysis established a wide array of seasonal and management risks factors, while multivariable modelling demonstrates that parity and RFM are the important cow level drivers of endometrial inflammation. Population-level metrics has established that although RFM appears to be more severe anomaly, the widespread distribution of multiparous animals within the herds causes multiparity to exert a greater overall burden on the herd’s reproductive health (PAF=48.3%). Finally, the strong internal predictive performance (AUC=0.794) of our multivariable model confirms that combining simple, farm-gate operational records provides a highly reliable early screening tool. This diagnostic approach allows managers/farmers to identify cows at high-risk early enabling preventive strategies to optimize reproductive health management without immediately requiring, invasive diagnostic interventions.

ACKNOWLEDGEMENT

The present study was supported by the funding agencies Rashtriya Krishi Vikas Yojana (RKVY) and Uttar Pradesh Council of Agricultural Research (UPCAR), Lucknow.
 
Disclaimers
 
The views and conclusions included in the present study belong to the authors and authors are solely responsible for the accuracy of the information provided, but do not accept liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
All the animal procedures for experiments were approved by the Institutional Animal ethical Committee as per approval number (IAEC/SVPUAT/2025/177).

CONFLICT OF INTEREST

The authors declare no conflict of interest regarding publication of the present article. No funding or sponsorship influenced the design of the present study, data collection analysis, decision to prepare and publish the manuscript.

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    Full Research Article

    Study on the Risk Factors Analysis for Subclinical Endometritis in Cohort of Repeat Breeder Indigenous Cattle

    A
    Ashutosh Tripathi1
    https://orcid.org/0000-0002-8511-9099
    M
    Manish Kumar Shukla1
    https://orcid.org/0000-0002-5443-1493
    A
    Atul Kumar Verma1
    https://orcid.org/0009-0007-6156-6525
    N
    Neelesh Kapoor2
    https://orcid.org/0000-0002-2201-3465
    V
    Vijay Singh1
    https://orcid.org/0009-0007-3842-1854
    A
    Arbind Singh3,*
    https://orcid.org/0009-0002-8821-5032
    R
    Rajbir Singh4
    https://orcid.org/0009-0004-1418-0580
    1Department of Veterinary Gynaecology and Obstetrics, College of Veterinary and Animal Sciences, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
    2Department of DNA Fingerprinting, College of Biotechnology, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
    3Department of Livestock Farm Complex, College of Veterinary and Animal Sciences, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250 110, Uttar Pradesh, India.
    4Department of Animal Husbandry, School of Agricultural Science, IIMT University, Meerut-250 001, Uttar Pradesh, India.

    ABSTRACT

    Background: The subclinical endometritis (SCE) has severe negative impacts over the fertility in bovines. There are several risk factors identified which predispose animals to SCE. The model development to evaluate the risk factors and population level impact in cohort of indigenous repeat breeder cows may be helpful for identification of cows at high risk of developing SCE.

    Methods: A total of 90 indigenous cattle with the history of repeat breeding were screened for subclinical endometritis (SCE) through endometrial cytology technique. The 5% cut off value of PMNs was established as threshold for SCE. Events likely to predispose SCE were taken in to consideration to perform a risk factor analysis using univariate logistic regression and multiple logistic regression model building. The population attributable fraction was calculated to chalk out the final risk factors among all.

    Result: The baseline disease prevalence established was 32.22% (29/ 90). Univariate analysis and multivariable logistic regression identified parity and retained fetal membranes (RFM) as the only independent risk factors to SCE. Multiparous status increased the odds of subclinical endometritis 7.75-fold (OR=7.75; 95% CI: 1.81-33.33; RR=2.75). RFM triggered even severe risk inflation (OR=15.87; 95% CI: 2.99-83.33; RR=4.60). The population attributable fraction (PAF) metrics showed that multiparity caused 48.3% of all cases, while RFM accounted for 43.2% of the cases. The multivariable framework demonstrated strong predictive validation with area under the curve (AUC) of 0.794. The study concluded that RFM is the more deadly pathology, therefore wide preventative strategies targeting the multiparous cows may yield the highest herd-level reduction in disease burden. This model may offer an accurate, cost-effective and even early warning filter for farm managers and the farmers.

    INTRODUCTION

    Subclinical endometritis (SCE), is the endometrial inflammation without overt clinical signs (Sheldon et al., 2019). Inadequate detection and management of SCE lead suboptimal conception rates and prolonging the calving intervals (Barajas et al., 2018). The prevalence reported for SCE is high (Sheldon et al., 2019), with negative impacts on reproductive performance (Gilbert et al., 2005; Barajas et al., 2018). The physio-pathological mechanism includes chronic inflammatory response of endometrium to long term persistent bacterial infections. The risk factors/etiology of SCE are still under debate that may include unspecific infection in the uterus, or persistent inflammatory response of the endometrium to bacterial infections (Wagener et al., 2017). Many risk factors for SCE, like Negative energy balance (NEB), metabolic diseases, uterine infections during the puerperium period, calving assistance (CA) and RFM, have been identified previously in large-scale dairies (Dubuc et al., 2010; Cheong et al., 2011; Wagener et al., 2017; Yanez et al., 2022). Therefore, the present study is undertaken based on the novelty in the development of a multivariable predictive model for SCE in cattle, establishing multiple significant risk factors to estimate disease probability at the individual animal level. This approach provides a potential decision-support tool for early identification of affected animals and may complement existing diagnostic strategies for improving reproductive management in dairy herds.

    MATERIALS AND METHODS

    Selection of animals
     
    Ninety repeat breeder (RB) indigenous cows (BCS 3 or more) from District Meerut and nearby areas were selected for the current research. Thereafter visual inspection of the discharge and rectal palpation were performed, as well as transrectal ultrasound to evaluate the reproductive tract (6.5 MHz linear-array transducer, Minitube portable USG). All these animals were screened for SCE through cytobrush technique (Wagener et al., 2017). All the animals under study were managed under optimum herd and nutritional managemental conditions.
     
    Diagnosis of subclinical endometritis
     
    The endometrial cytology was performed using modified cytobrush gun (Mayfair technologies, Ludhiana). The cytology gun fitted with brush was inserted aseptically into the uterine body, thereafter the plunger was pressed to externalize the cytobrush inside the uterine body, which was rotated gently against the uterine wall to collect an endometrial sample. Following collection of the sample, the cytobrush was retracted back in the rod to avoid contamination. Thereafter cytobrush was recovered and slide was prepared by rolling it on the glass slide and allowing it to air-dry. The slides were stained using field’s stain and then examined at 400X magnification. The cut off value of PMNs was ≥5% for declaring animal positive for SCE (Wagener et al., 2017).
     
    Risk factors
     
    The puerperium and post-partum events were recorded based on the history and observation and the events of interest were recorded to determine the possible risk factors for SCE. Five age group categories were included in the present study: 4-5, 5-6, 6-7, 7-8 and 8-9 years. Five calving seasons (CS) within the year were taken into consideration for statistical analysis: the rainy season, autumn, spring, summer and winter. Two parity categories were included: primiparous and multiparous. The assisted delivery, RFM, calf sex and abortion were also recorded in the current study. Whether the animals suffered with any kind of uterine diseases (UD) first 3 weeks post-partum or not was also included in the study.
     
    Statistical analysis
     
    In the present study descriptive statistics using SPSS were utilized to summarize the distribution of explanatory variables and the status of subclinical endometritis (SCE). The frequencies and percentages were calculated for all categorical variables. The association between individual explanatory variables and SCE was assessed using the chi-square test, while fisher’s exact test was applied when any cell’s expected frequency in any of the cell was <5. The variables that reveal significant (P≤0.35) association with SCE in univariate analysis were further considered for multivariable analysis. Therefore, in the first stage, univariate logistic regression was used to screen potential predictor variables using P≤0.35 as described by Villasenor-Gonzalez et al. (2025) and in the second stage, these variables were entered into a multivariable logistic regression model with backward elimination and a retention criterion of P<0.10. A relatively liberal screening threshold is commonly recommended in epidemiological and regression modeling studies to avoid excluding variables that may not appear significant in univariate analysis but may become important after adjustment for confounding in multivariable analysis. Under univariate binary logistic regression, crude odds ratios (OR) and their 95% confidence intervals (CI) were estimated. A backward stepwise elimination method was employed, retaining variables with P<0.05 in the final model. Adjusted odds ratios (OR) with 95% confidence intervals were derived from the final model. Relative risks (RR) were estimated from adjusted odds ratios using the approach described by Zhang and Yu (1998). The proportion of exposed animals (Pe) and the population attributable fraction (PAF) were calculated to evaluate the relative contribution of significant risk factors to the occurrence of SCE. Statistical significance was determined at P<0.05. The multicollinearity among the explanatory variables was evaluated using the Variance Inflation Factor (VIF) and no evidence of problematic multicollinearity was observed (all VIF values <5).

    RESULTS AND DISCUSSION

    Explanatory variables and cohort baseline epidemiology
     
    The 5-6 years age group showed dominant footprint, accounting for 35.6% (n=32) of the cows. Parity classification including primiparous and multiparous was relatively balanced, comprising 46.7% (n=42) and 53.3% (n=48) of the herd, respectively. The highest incidences were recorded during the rainy season (26.7%, n=24), followed by spring (23.3%, n=21) and winter (21.1%, n=19). The majority of animals had calving without requiring assistance (82.2%, n=74) and 96.7% (n=87) of the animals had no abortion history. The RFM and post-partum uterine diseases were diagnosed in 21.1% (n=19) and 22.2% (n=20) of the animals, respectively. The male and female calves comprised of 53.3% (n=48) and 46.7% (n=42) of the total births. The prevalence of SCE was established at 32.2% (n=29), leaving a baseline of 67.8% (n=61) consisting of negative cases (Table 1).

    Table 1: Distribution of explanatory variables and outcome status.


     
    Univariate screenings and predictive associations
     
    Univariate relationship screening through chi-square and fisher’s exact tests established highly significant associations (p<0.05) among SCE and primary explanatory variables, excluding the abortion history (p=0.242) and sex of the calf (p=0.809) (Table 2). Fisher’s exact test was rigorously substituted across parameters where low cell frequencies violated standard chi-square distributional assumptions (Age group and abortion history).

    Table 2: Association of explanatory variables with subclinical endometritis.


           
    To isolate localized directional effects, individual variables were evaluated using univariate binary logistic regression (Table 3). Parity (p=0.004), calving season (p=0.099), calving assistance (p<0.001), abortion history (p=0.231), RFM (p<0.001) and uterine disease (p<0.001) exhibited strong regression footprints. Applying an expansion threshold constraint of (p≤0.35) to capture weak or confounding interactions, parity, calving assistance, abortion history, RFM and uterine disease were cleared for multivariable entry (Villasenor-Gonzalez et al., 2025) through backward stepwise selection method.

    Table 3: Univariate logistic regression analysis of factors associated with subclinical endometritis.



    Multivariable logistic regression and model diagnostics
     
    The independent risk configuration was mapped using multivariable logistic regression utilizing a backward stepwise selection sequence. Independent variables with non-significant structural impact were iteratively stripped, whereas variables with true predictive strength (p<0.05) were retained in the final framework.
           
    The final model identified parity and RFM as the independent predictors of SCE. Model diagnostic tests showed that removing parity from the equation significantly compromised the overall model fit (Change in -2 Log Likelihood=9.166; p=0.01). Similarly, the removal of RFM caused an even more drastic collapse in model performance (Change in -2 Log Likelihood=29.047; p<0.01), proving that both risk parameters are independently and highly significantly (p<0.01) tied to SCE development.
           
    The definitive effect points and precision estimates are mentioned in Table 4. Multiparous animals demonstrated a 7.75-fold inflation in the odds of developing subclinical endometritis relative to primiparous animals (OR=7.75; 95% CI: 1.81-33.33). Furthermore, the presence of RFM generated a profound impact, with affected animals exhibiting a 15.87-fold increase in disease odds compared to their non-retained counterparts (OR=15.87); 95% CI: 2.99-83.33).

    Table 4: Multivariable logistic regression model for risk factors associated with subclinical endometritis.


           
    The corresponding true epidemiological risks showed a Relative Risk (RR) of 2.75 (95% CI: 1.31-5.78) for multiparity and an RR of 4.60 (95% CI: 2.71-7.80) for RFM. The PAF for multiparity and RFM stood at 48.3% and 43.2%, respectively (Fig 1). Predictive performance assessment yielded an area under the curve (AUC) of 0.794, demonstrating a strong, internally validated diagnostic fit (Fig 2).

    Fig 1: Population attributable factor (Multivariable logistic regression) for risk factors associated with SCE.



    Fig 2: Receiver operating characteristic (ROC) curve validating the multivariable diagnostic risk model.


           
    Final analysis through multivariable logistic regression established both parity and RFM as significant risk factors for developing SCE (Fig 3). Multiparous cows had 7.75 times higher odds of developing SCE compared to primiparous (OR=7.75; 95% CI: 1.81-33.33). Similar to this, animals affected with RFM exhibited 15.87 times higher odds of SCE than those not (OR=15.87; 95% CI: 2.99-83.33).

    Fig 3: Multivariable logistic regression model for risk factors associated with SCE.


           
    The chances of encountering SCE were found 2.75 times higher among multiparous cows (RR=2.75; 95% CI: 1.31-5.78) and it was 4.60 times higher in animals affected with RFM (RR=4.60; 95% CI: 2.71-7.80). The PAF indicated that out of total, 48.3% and 43.2% of SCE cases could be attributed to the multiparity and RFM, respectively. Also, high PAF for these both conditions (aiming to reduce or treat) suggest need for preventive strategies against these two risk factors so as to deal with SCE.
           
    Several herd-level risk factors to SCE like CS, RFM, UD, parity, metabolic diseases like negative energy balance, calving assistance, calf sex and timing of sampling after parturition have been identified in previous studies (Kasimanickam et al., 2004; Dubuc et al., 2010; Cheong et al., 2011; Prunner et al., 2014; Yanez et al., 2022; Villasenor-Gonzalez et al., 2025) with the prevalence reported ranging from 7 to 53% between 3rd to 7th week post-partum (Quintela et al., 2018; Villasenor-Gonzalez et al., 2025). Among routine screened animals, prevalence reported was 6.35% (Narwade et al., 2026), while among repeat breeder cows, the prevalence reported is between 12.7 to 52.7% (Salasel et al., 2010; Janowski et al., 2013; Pothmann et al., 2015; Pascottini et al., 2017; Wagener et al., 2017; Bedewy and Rahawy, 2019; Emre, 2024; Pande et al., 2025; Villar et al., 2025).
           
    Risk factors analysis in a previous study for SCE in RB cows including, season, parity, reproductive pathologies, no variable revealed significant effect, however in the present study the parity and RFM showed significant footprints. However similar to previous study numerical difference in case of season and reproductive pathology were noticed in present study too (Villar et al., 2025). However, in other studies over effect of reproductive pathologies, statistically higher incidence of SCE was noticed among animals suffering with post-partum reproductive pathologies (Salasel et al., 2010). The effect of parity in occurrence of SCE has also been observed in previous studies similar to the present study. Also, in those studies during first artificial insemination the month/season of AI also seem to affect significantly the occurrence of SCE (Pascottini et al., 2017; Diaz-Lundahl et al., 2021) however in the present study season during calving does not seem to affect occurrence of SCE similar to previous study (Prunner et al., 2014) however another study differs with this opinion (Villasenor-Gonzalez et al., 2025). In previous studies uterine infections and CA were identified as risk factors (Dubuc et al., 2010; Cheong et al., 2011; Villasenor-Gonzalez et al., 2025) but the present study CA didn’t seem to affect occurrence of SCE. RFM may be a sequence from CA and obstetrical procedures (Montiel-Olguin et al., 2019). RFM, altering the immunity status (Chebel, 2021) may be the reason for it being the predisposing factor for SCE. The multiparity in cows lead to increased uterine trauma and slower involution following repeated calvings. Also, reduced postpartum immune competence, higher post-partum disorders (e.g. RFM), greater metabolic stress and impaired bacterial clearance may potentially predispose multiparous cows to SCE (Kasimanickam et al., 2004; Sheldon et al., 2006; LeBlanc, 2008; Sheldon et al., 2009; LeBlanc, 2012).
           
    The indigenous cattle are often raised by marginal/ smallholders, where routine postpartum reproductive examinations are often impractical because of limited veterinary access and financial constraints, the established risk factors may be used to prioritize the animals at risk for timely examination and proper intervention. This model/ targeted strategy may enable efficient use of veterinary services, may reduce unnecessary diagnostic costs and may support reproductive efficiency and farm profitability.

    LIMITATIONS

    The limitation of the present study includes relatively small sample size (n = 90), which may have limited the statistical analytical power and the precision of the estimated variable associations i.e. wide confidence interval. Also, the animals were based out of a single geographical region, which may have limited the generalizability of the findings to other geographical conditions. The indigenous cattle were included in the present study, therefore the established risk factors may not directly applicable to other crossbred or exotic breeds. The cross-sectional observational design allows identification of associations but does not permit causal inferences. It is recommended that future studies involving larger sample size, multi-regional sampling comprising different breeds and longitudinal study designs are warranted to further validate and extend the current findings.

    CONCLUSION

    There is significant prevalence of SCE among indigenous repeat breeder cattle. Preliminary univariate analysis established a wide array of seasonal and management risks factors, while multivariable modelling demonstrates that parity and RFM are the important cow level drivers of endometrial inflammation. Population-level metrics has established that although RFM appears to be more severe anomaly, the widespread distribution of multiparous animals within the herds causes multiparity to exert a greater overall burden on the herd’s reproductive health (PAF=48.3%). Finally, the strong internal predictive performance (AUC=0.794) of our multivariable model confirms that combining simple, farm-gate operational records provides a highly reliable early screening tool. This diagnostic approach allows managers/farmers to identify cows at high-risk early enabling preventive strategies to optimize reproductive health management without immediately requiring, invasive diagnostic interventions.

    ACKNOWLEDGEMENT

    The present study was supported by the funding agencies Rashtriya Krishi Vikas Yojana (RKVY) and Uttar Pradesh Council of Agricultural Research (UPCAR), Lucknow.
     
    Disclaimers
     
    The views and conclusions included in the present study belong to the authors and authors are solely responsible for the accuracy of the information provided, but do not accept liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    All the animal procedures for experiments were approved by the Institutional Animal ethical Committee as per approval number (IAEC/SVPUAT/2025/177).

    CONFLICT OF INTEREST

    The authors declare no conflict of interest regarding publication of the present article. No funding or sponsorship influenced the design of the present study, data collection analysis, decision to prepare and publish the manuscript.

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