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Differential access of healthcare services and its impact on women in India: a systematic literature review

• Review Paper
• Published: 13 January 2023
• Volume 3 , article number  16 , ( 2023 )

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• Sumanjeet Singh 1 ,
• Binod Rajak   ORCID: orcid.org/0000-0003-0857-4474 2 ,
• Ranjit Kumar Dehury 3 ,
• Swati Mathur 4 &
• Akankhya Samal 5  

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The right to healthcare services is considered the highest entitlement in India, with the help of economic prowess. However, after 75 years of independence, the constraints for health services across the gender still exist glaringly, with more discrimination against women. Indian women still struggle to receive proper healthcare facilities compared to their male counterparts, hindering women’s social and economic growth. The current paper discusses a range of literature reviews using a systematic approach according to PRISMA guidelines to find the nuances in accessing healthcare services in the Indian context for women. We searched for a combination of gender and health, such as accessibility, utilization, health-seeking behaviour, healthcare delivery, availability of healthcare services, gender disparity, and barriers in women's healthcare services. The term India also combined with them to unearth all relevant literature. Finally, around 80 research papers were collected and reviewed from different search engines such as Google Scholar, Scopus, Web of Sciences, and PubMed for assessing the gender angle in healthcare. The paper presents an outline of a theoretical framework with the help of healthcare services' demand and supply side. The paper adopted the health systems approach in its theoretical framework. The paper suggests revisiting existing schemes, policies, and programs regarding gender issues, which help identify solutions and recommendations for policymakers and government bodies.

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Ramjas College University of Delhi, Delhi, 110007, India

Sumanjeet Singh

Department of Management Studies, NALSAR University of Law, Hyderabad, Telangana, 500101, India

Binod Rajak

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Ranjit Kumar Dehury

Department of Management Institute of Public Enterprise Hyderabad, Hyderabad, Telangana, 500101, India

Swati Mathur

Centre for Studies in Economics and Planning (CSEP), School of Social Sciences Central, University of Gujarat Gandhinagar-382030, Gujarat, India

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Singh, S., Rajak, B., Dehury, R.K. et al. Differential access of healthcare services and its impact on women in India: a systematic literature review. SN Soc Sci 3 , 16 (2023). https://doi.org/10.1007/s43545-023-00607-9

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Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data

• Mayank Singh 1 ,
• Anuj Singh 2 &
• Jagriti Gupta 3  

BMC Public Health volume  24 , Article number:  2514 ( 2024 ) Cite this article

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This paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data.

National Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods.

Our analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns.

In conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

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Cesarean delivery, also known as a C-section, is a surgical procedure in which a baby is delivered through an incision made in the mother’s abdomen and uterus. This procedure is usually reserved for situations where vaginal delivery is not possible or safe for the mother or baby. In recent years, the rate of C-sections has increased worldwide, raising concerns about the potential risks and benefits of this procedure [ 1 , 2 ]. According to the World Health Organization (WHO), the ideal rate for C-sections is between 10% and 15% of all births [ 3 ]. The global average C-section rate has shown a steady increase over the years and currently stands at approximately 21% of all births. Furthermore, projections indicate that this trend is set to persist over the next decade, with an estimated 29% of all births expected to be delivered by C-section by the year 2030 [ 4 ]. Recent studies have shown that C-section delivery rates have also been increasing in India, in recent years. The number of C-section deliveries has more than doubled in India as a whole, from 8% in 2005-06 to 17% in 2015-16 [ 5 , 6 ]. This trend has been attributed to various factors, including changes in maternal and fetal indications for C-sections, changes in maternal preferences, and changes in healthcare policies and practices [ 7 , 8 , 9 ].

Globally, “over half a million maternal deaths occur every year” the majority of which take place in developing countries [ 10 ]. When pregnancy complications arise during pregnancy, this surgical intervention is considered a life-saving procedure to reduce maternal and neonatal mortality as in the rest of the world, India has also observed an increase in C-section delivery rates. Moreover, it is apparent that the rising rates of C-section deliveries are linked to several health consequences. Here are some negative health outcomes and some concerns about the increasing C-section delivery rates:

Increased maternal mortality : Women who undergo C-section deliveries have a higher risk of maternal mortality than those who deliver vaginally, particularly if the C-section is not medically necessary. The study also found that C-section rates vary widely across states in India, ranging from 7 to 49%, suggesting that overuse of C-sections may be contributing to the higher maternal mortality rates [ 11 ].

Increased neonatal mortality and morbidity : infants born by C-section have a higher risk of neonatal mortality and morbidity than those born vaginally, even after accounting for differences in maternal risk factors. The study suggests that the overuse of C-sections may be contributing to these negative outcomes [ 12 ].

Financial burden : C-section deliveries are more expensive than vaginal deliveries, both for the healthcare system and for individual families. The average cost of a C-section delivery in India was almost three times higher than the cost of a vaginal delivery, and that family who had C-sections experienced greater financial burden and were more likely to face catastrophic healthcare expenditure [ 13 ].

Unnecessary interventions : Overuse of C-sections can lead to unnecessary medical interventions and procedures, such as induction of labor and early delivery. These interventions can have negative consequences for maternal and infant health, including increased risk of infection and neonatal respiratory distress syndrome [ 14 , 15 ].

There are several other negative health outcomes associated with infants delivered by C-section, including childhood obesity, respiratory disorders, type 1 diabetes, acute lymphoblastic leukemia, impaired cognitive development, higher autism rates, and an increased risk for neurodevelopmental disorders [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].

In South India, the rates of C-sections are generally higher than the national average, and there are several factors that contribute to this trend which is a cause for concern. The C-section rate was higher in urban areas than in rural areas, and the most common reason for C-sections was “previous C-section” [ 15 ]. Another study found that the C-section rate was higher in women who were of higher socioeconomic status and who had received antenatal care [ 24 ]. A study found that the C-section rate in south Indian states/UTs and districts were found to be very high, particularly in the private sector hospitals [ 25 ]. Therefore, the most common reason for C-sections was “previous C-section” and women who had C-sections had longer hospital stays and higher healthcare costs. Also, the C-section rate in a tertiary care hospital in South India was 75%, and that the most common reason for C-sections was “fetal distress”. In this context, it is important to understand the reasons for the high rates of C-sections in South India and the implications of this trend on maternal and neonatal health outcomes. It is important for healthcare providers and policymakers to address the overuse of C-sections in South India and promote evidence-based guidelines and practices, encouraging vaginal birth after cesarean (VBAC) when appropriate, providing comprehensive childbirth education and support, and ensuring access to quality prenatal and obstetric care. Furthermore, the WHO has also called for efforts to reduce unnecessary C-sections and ensure that the procedure is only used when medically necessary to improve maternal and neonatal outcomes.

Data and methods

Data source.

This is a secondary data analysis of The National Family Health Survey (NFHS), which is a nationally representative survey conducted in India to collect comprehensive data on various aspects of population, health, and nutrition. NFHS-5 is the fifth round of the survey conducted between 2019 and 2021. Data is representative at the district level also. The unit of analysis is the individual. It provides crucial information on maternal and child health, reproductive health, family planning, and healthcare services utilization, including data on deliveries. These may include the mode of delivery (C-section or vaginal delivery), the type of healthcare facility where the delivery occurred (public hospital, private hospital, clinic, home), and socio-demographic factors of women such as age, education, marital status, and wealth status.

Sample selection

For our study, we selected a group of women who had given birth at least once in the past five years, resulting in a total of 232,920 births (Supplementary Figure S1 ). Out of these, 56,077 were second or higher-order births, so the count for the last birth in the last 5 years was 176,834. Since we only included data from their most recent pregnancy, we excluded all second and higher-order pregnancies. We also excluded non-institutional births, leaving us with a sample of 155,624 eligible births, with their mode of delivery categorized as C-section (yes or no). Afterward, we excluded all those that were not in South India, resulting in a final sample of 22,403. “South India” typically refers to the southern region of the country, encompassing specific states such as Andhra Pradesh, Telangana, Karnataka, Kerala, Tamil Nadu, and the union territory of Puducherry, Andaman & Nicobar Island, and Lakshadweep (Supplementary Table S1 ). Out of these, 44.96% underwent a C-section delivery, while the remaining had vaginal deliveries. Figure-S1 provides a more detailed explanation of the sample selection process.

Variable description

Outcome variable.

The current study focuses on the last delivery, using a binary outcome variable to assess the mode of delivery among currently married women aged 15 to 49 years. This is based on the mother’s self-report. Given the importance of caesarean deliveries as an indicator of maternal health and healthcare access, their inclusion as the primary outcome variable is.

Explanatory variables

This study utilized several socio-demographic characteristics as individuals and household level variables.

Individual characteristics - These included Mother’s age at child birth, categorized as < 20 years, 20–29 years and 30 and above years. Mother’s schooling as no education, primary education, secondary education, and higher education. Age at marriage, categorized as less than 18 years, 18–24 years, and 25 years or older. Other variables included pregnancy problems (no or yes), High risk fertility behavior (No risk, single risk, multiple risk), registration with Auxiliary Nurse and Midwife (ANM) (yes or no), place of delivery (government hospital or private hospital), ANC visits (less than or equal to three visits or more than three visits), ever pregnancy termination (no or yes), Size at childbirth (bigger than normal, normal, less than normal). A woman is classified as exhibiting high-risk fertility behaviour if she gives birth at less than 18 or above 34 years old, has a birth interval of less than 24 months, or has a birth order of 4 and higher. A woman is considered to have a single high-risk fertility behaviour if she reports experiencing one of the following: giving birth at a younger age (less than 18 years) or above 34 years, or having a birth interval of less than 24 months, or having a high birth order (four and above). Multiple high-risk fertility behaviours are identified when a woman exhibits a combination of at least two of the aforementioned behaviours [ 26 , 27 , 28 , 29 ]. Furthermore, the pregnancy problem variable was derived from the following indicators: vaginal bleeding (yes or no), convulsions (yes or no), prolonged labor (yes or no), severe abdominal pain (yes or no), and high blood pressure during pregnancy (yes or no). If any of the mentioned issues were present during pregnancy, it is classified as a pregnancy problem; otherwise, it is categorized as not having a pregnancy problem.

Household characteristics - Household characteristics encompass wealth index (poorest to richest), place of residence (rural or urban), religion (Hindu or non-Hindu), and social status (Scheduled Caste (SC)/Scheduled Tribe (ST), Other Backward Class (OBC), others).

Statistical analysis

Bayesian multilevel logistic regression model.

Since the predicted variable is dichotomous (C-section delivery “Yes” or “No”), a binary logistic regression model was used. Multilevel logistic regression includes random effects as an extension of the single-level logistic regression model [ 30 ]. Suppose we have data consisting of last birth delivery information of women, (level one) grouped into characteristics (level two, three and four). Let \({Y}_{ij}\) be the binary response for C-section delivery in region j and \({X}_{ij}\) be an explanatory variable. We define the probability of the response equal to one \({\pi}_{ij}=P({y}_{ij}=1)\) Where; \({\pi}_{ij}\) be modeled using a logit link function. The standard assumption is that \({Y}_{ij}\) has a Bernoulli distribution. Then, the two-level models are given by:

\({X}_{i}=({X}_{1ij,}{X}_{2ij,}\dots\:\dots\:\dots\:.{X}_{kij})\) represent the level of covariates, for variable k ( \(\beta={\beta}_{o},{\beta}_{1},\dots\:\dots\:{\beta}_{k})\) are the regression parameter coefficient. The parameters \({U}_{0j},{U}_{1j},\dots\:\dots\:.,{U}_{kj}\) is the random effect of the model parameter at different levels. With the assumption \({U}_{hj}\) , follows a normal distribution with mean zero and variance \({\sigma}_{u}^{2}\) .

Multilevel analysis of null model

A binary outcome variable with an empty three-level model represents a group of individuals.

And provides a distribution of group-dependent probabilities without considering any further explanatory variables [ 30 , 31 ]. This model only contains random groups and random variation within groups. It can be expressed with logit link function as follows.

Where \({\beta}_{0}\) indicates the population average of the transformed probability and \({U}_{0j}\) is the random deviations from this average for region j

Model selection and comparison

In model selection, the best model is selected from a set of options based on its performance. The deviance information criterion (DIC) is a widely used statistic for comparing models in a Bayesian context. Deviance is defined as

where y represents the data, θ denotes the unknown parameters of the model, and \(p\left(y|\theta\right)\) is the likelihood function. The constant c, which cancels out in all calculations when comparing different models and thus it is not required to be known.

The expectations, denoted as \(\widehat{D}=E\left[D\left(\theta\right)\right]\) , serve as a measure indicating how well the model fits the data; a higher value suggests a poorer fit. The deviance information criterion (DIC) is defined as \(\text{DIC}=\widehat{D}+pD\) . Since the deviance (D) decreases with an increasing number of parameters in a model, the pD term compensates for this effect by favoring models with fewer parameters.

DIC has an advantage over other Bayesian model selection criteria, such as AIC and BIC, in that it can be easily calculated from samples generated by a Markov Chain Monte Carlo (MCMC) simulation. Unlike AIC and BIC, which require calculating the likelihood at its maximum, an information that is not readily available from the MCMC simulations. To compute DIC, simply calculate \(\widehat{D}\) as the average of \(D\left(\theta\right)\) over a sample value of \(\theta\) , and \(D(\widehat{\theta})\) as the value of \(D\) evaluated at the average of the samples of \(\theta\) [ 32 ]. The DIC is then derived directly from these approximations.

Geospatial analysis

The Moran’s I values measure the spatial autocorrelation or the degree of similarity between neighboring districts regarding the specific indicator/variable. The values range from − 1 to 1, where a positive value indicates positive spatial autocorrelation (similar values tend to cluster together), a negative value indicates negative spatial autocorrelation (dissimilar values tend to cluster together), and a value close to zero indicates no spatial autocorrelation. Univariate Cluster map depicts the four major category of colour code namely,

High-high clustering (Hot Spot): High prevalent location (district) surrounded by high prevalent neighborhood district.

Low-low clustering (Cold Spots): Low prevalent location (district) surrounded by low prevalent neighborhood district.

High-low clustering (Spatial outliers): High prevalent district surrounded by the low prevalent district.

Low-high clustering (Spatial outlier): Low prevalent district surrounded by the high prevalent district.

Bivariate LISA (Local Indicators of Spatial Association) maps utilize specific indicators to reveal spatial patterns and relationships between two variables. These indicators include:

High-High (HH) Clustering: Indicates areas where both variables exhibit high values and are spatially clustered. These regions highlight locations with similar high values for both variables.

Low-Low (LL) Clustering: Represents areas where both variables have low values and are spatially clustered. These regions identify locations with similar low values for both variables.

High-Low (HL) Clustering: Signifies areas where one variable has a high value and the other has a low value, indicating a spatial outlier or dissimilarity between the variables.

Low-High (LH) Clustering: Represents areas where one variable has a low value and the other has a high value, indicating another form of spatial dissimilarity.

Ethical consideration

Our research relies on survey data that has undergone anonymization, ensuring the removal of any identifiable information associated with individuals. Prior to participating in the survey, all participants provided informed consent, and data collection was conducted in a confidential manner. The Measure DHS International Program has granted written permission for the usage of the data, and the dataset has been publicly released. Therefore, there is no requirement for additional permission to utilize the dataset.

Patient and public involvement

Figure  1 presents the visual representation of the regional distribution of C-section deliveries throughout India for the years 2019–2021. The outcomes depicted in Fig.  1 highlight distinct variations in the occurrence of C-section deliveries across different geographical regions of India within the specified time frame, showing the elevated C-section rate in the South Indian states.

Regional prevalence of C-section delivery in India 2019-21

Socio-demographic characteristics and its association of C-section deliveries

Table  1 shows the prevalence of C-section deliveries among women in South India, analyzed by selected background characteristics, establishing their associations through chi-square statistics at the 95% level of significance. Within the presented data, several key variables exhibit statistically significant relationships with the dependent variable. Notably, as the maternal age at childbirth progresses, there is a concurrent escalation in C-section deliveries, with the highest prevalence observed among mothers aged above 30 years (53.5%). Furthermore, there is positive correlation between a mother’s level of schooling and prevalence of C-section. As the years of formal education ascend, so does the prevalence of C-section, ranging from 30.7% in women with no formal education to an elevated 53.6% among those with a higher educational background. Additionally, the healthcare facility type emerges as a noteworthy factor, with private hospitals demonstrating a notably higher prevalence of C-section deliveries at 59.8%, in contrast to government hospitals where the prevalence is 33.9%. Curiously, women exhibiting high-risk fertility behavior display a negative relationship with C-section deliveries. Those classified as no-risk (46.4%) and single-risk (41.1%) exhibit a higher prevalence of C-section deliveries in comparison to their counterparts with multiple risk factors, whose rate stands at 27.6%. Moreover, there exists a positive correlation between a woman’s age at marriage and the likelihood of opting for C-section deliveries. As the age at marriage advances, there is a corresponding increase in the prevalence of C-section deliveries. Furthermore, the wealth index of women demonstrates a positive association with C-section deliveries in South India. As the financial status of women improves, the prevalence of C-sections escalates: poorest (25.9%), poorer (34.8%), middle (44.3%), richer (47.0%), and richest (52.5%). Lastly, urban women (47.9%) and those adhering to the Hindu faith (46.0%) exhibit a higher prevalence of C-section deliveries compared to their rural counterparts and women following non-Hindu religions in South India. These findings collectively contribute to a more nuanced understanding of the intricate factors influencing C-section delivery choices within this region.

Bayesian multilevel analysis of C-section delivery by background characteristics

The Bayesian Multilevel analysis model incorporates the null model, individual-level model, household-level model, and the full model comprising both individual and household levels. Individual level model takes into account the characteristics mother’s age at birth, mother’s schooling, pregnancy problem, registered with ANM or not, place of delivery, high risk fertility behavior, number of ANC visits, age at marriage, ever pregnancy termination and size of the child. Household level models includes the characteristics such as wealth-index, residence, religion and social status. The full model considers the sum of individual level characteristics and household characteristics. The odds ratios (ORs) and their corresponding 95% credible intervals (Cr.I) are reported for each characteristic in each model.

Table  2 shows Bayesian multilevel analysis at various levels (State, District, and PSU) for different set of predictor variables. These includes individual level, household level and combined (individual + household) level variables, aiming to predict the C-section occurrences for last birth based on background characteristics in South India, 2019-21. The presence of significant non-zero variance at different levels in the null model suggests that C-section delivery varies across different levels in India. Therefore, multilevel analysis can be considered an appropriate approach for further examination.

Individual level model

From this model, we have found that the mother’s age at child birth, mother’s schooling, place of delivery, age at marriage, ever pregnancy termination and size of the child were the significant predictors for higher likelihood of C-section delivery in South India.

The likelihood of C-section delivery increases with the age of the mother at childbirth. For example, mothers aged 20–29 years during their childbirth had a 13% (95% Cr.I. 1.09–1.16) higher likelihood, and those aged 30 years and above had a 50% (95% Cr.I. 1.40–1.62) higher odds of C-section delivery compared to the reference category. Similarly, an increase in the level of mother’s education was associated with an increased odds of C-section delivery. For instance, primary, secondary, and higher educated women had 1.24 (95% Cr.I. 1.14–1.35), 1.53 (95% Cr.I. 1.40–1.67), and 1.58 (95% Cr.I. 1.42–1.76) times higher odds of C-section delivery, respectively, compared to uneducated women. Delivery in a private hospital was significantly associated with C-section delivery, with 3.2 times higher odds compared to government hospitals. The age at marriage of women showed a positive relationship with C-section delivery, with a 15% higher likelihood in women married between age 18-24-year and a 64% higher likelihood among women married after age 25 years compared to the reference category women married below 18 years. Furthermore, women who had ever terminated their pregnancies had 21% higher odds of C-section delivery compared to ever non-terminated pregnancy women. The size of the child also played a significant role in C-section delivery, with less than normal size and greater than normal size children having odds ratios of 1.16 and 1.15, respectively, for C-section delivery compared to women who gave birth to a normal-sized child.

Household level model

The findings from the household level model indicate that wealth-index, residence, religion, and social status are significantly associated with C-section delivery. The wealth-index exhibits a positive relationship with C-section delivery, indicating that as the household’s wealth improves, the odds of having C-section deliveries also increase. Specifically, the adjusted odds ratios (AOR) were 1.6 (95% Cr.I. 1.40–1.82), 2.25 (95% Cr.I. 2.07–2.49), 2.75 (95% Cr.I. 2.48–3.10), and 3.33 (95% Cr.I. 2.96–3.77) for poorer, middle, richer, and richest wealth indices, respectively in compare to poorest women. Urban households’ women had a 7% higher likelihood of C-section delivery compared to rural households women’s. Similarly, Hindu household women had an AOR of 1.25, while OBC and Others household women had AORs of 1.17 and 1.18 odds of C-section delivery, respectively in compare to their reference group of women.

Individual + household (full model)

From Model 4 (full model), we found that factors such as pregnancy problems, registration with ANM, number of Antenatal Care (ANC)visits, and place of residence were not significant predictors of C-section delivery in South India. Similar to the individual-level model, the full model also demonstrated a significant association with an increase in the age at marriage. Additionally, with an increase in years of schooling, the likelihood of C-section delivery also increased. For instance, women with primary education had 14% higher odds, and women with secondary and higher education had odds of 1.44 for C-section delivery compared to uneducated women. Deliveries at private hospital had 3.28 times higher odds of C-section deliveries compared to government hospital deliveries. Age at marriage also played significant role in C-section deliveries, with 77% higher odds if mother’s age at marriage was greater or equals to 25 years. The size of the child was also played an important role in C-section deliveries if baby size was less than or greater than normal. Wealth index showed a positive correlation with C-section deliveries in South-India. As wealth improves, the odds of using C-section deliveries increases from 51% in poorer wealth category to 69% in richer wealth category and then decreases to 49% in the richest category.

The variance for state, district, and primary sampling unit (PSU) was also reported for each model, indicating the amount of variability in cesarean delivery rates at these levels that could not be explained by the included characteristics. The variance for these levels decreased in the full model (σ 2 state = 0.31, σ 2 district = 18, σ 2 PSU = 0.20) compared to the null model (σ 2 state = 0.39, σ 2 district2 = 24, σ 2 PSU = 0.24), suggesting that the included characteristics explained some of the variability.

The Supplementary Figure S2 shows prevalence of C-section deliveries in the districts of South India was analyzed using a spatial map. This map provides insights into variations in the prevalence of C-section delivery, highlighting areas with both high and low prevalence, and identifying potential disparities in the districts of South India. The interpretation of the spatial autocorrelation and hotspot analysis of C-section deliveries in South India, 2019–2021 involves examining the distribution and clustering patterns of C-section deliveries in the region. Moran’s I values (I = 0.62) indicated that there was significant clustering (99% confidence < 0.001) of C-section delivery in south India as a whole (Fig.  2 ). On the other hand, Hotspot analysis focuses on identifying statistically significant clusters of high values within a spatial dataset. In this study, hotspot analysis aims to identify areas in South India with a significantly higher prevalence of C-section deliveries compared to the expected average. This analysis can help identify spatially concentrated areas of concern or areas with potential over-prevalence of C-section deliveries.

Spatial Autocorrelation and hotspot analysis of C-section delivery in South India, 2019-21

Based on the Supplementary Table S2 , we observed that several indicators/variables show statistically significant spatial dependence in relation to C-section delivery prevalence at the district level in South India. For example, age at marriage ( > = 25 years), mother age at childbirth ( > = 30 years), and antenatal visits ( > = 4) exhibit high Moran’s I values and high Z scores, indicating strong positive spatial autocorrelation and significant clustering patterns. On the other hand, variables such as place of delivery (Private), richest, urban, pregnancy termination (Yes), and antenatal visits ( > = 4) show lower Moran’s I values and lower Z scores, suggesting relatively weaker spatial dependence.

Figure  3 shows the Emp. Bayes bivariate LISA cluster maps indicating the geographic clustering (hotspot & cold spots) of c section deliveries with different independent variables across the districts of South-India. Map A1 indicates the bivariate clustering of C-section with private place of delivery. Map A2 indicates the bivariate clustering of C-section delivery with age at marriage ( > = 25 years), A3 indicates the bivariate clustering of C-section delivery with mother age at childbirth ( > = 30 years) and A7 indicates the bivariate clustering of C-section delivery with antenatal Visits ( > = 4). The districts marked in red were clustered as high-high, signifying a high prevalence of C-section deliveries, while the districts in blue indicated low-low clustering, denoting a low prevalence of C-section deliveries along with their corresponding predictor variables.

Emp. Bayes Bivariate LISA cluster maps of South India showing the geographic clustering (hotspots & cold spots) of C-section delivery, 2019-21

The escalating global trend of cesarean section (C-section) deliveries has significantly impacted maternal and neonatal healthcare. Amid this landscape, the prevalence of C-section deliveries in South India during 2019–2021 emerges as a crucial area of investigation. Notably, maternal age at childbirth plays a significant role, with older mothers (30 + years) displaying a heightened likelihood of having C-sections. This study highlights the richest delivering via C-section, the plausible reasons may be that frequency of C-sections may be impacted by the accessibility and availability of medical facilities and qualified medical personnel [ 33 ]. The option of C-sections can be more accessible in areas with well-established healthcare infrastructure. There could be a number of reasons why C-sections are more common in these areas, such as convenience or perceived safety [ 34 ]. This rise in C-sections could be explained by the idea that once a caesarean, always a caesarean, as studies have shown that the majority of C-sections performed in hospitals are repeat procedures Subsequently, this trend aligns with global research, reflecting potential medical considerations and maternal preferences for safer deliveries [ 35 , 36 ]. It is worth exploring whether this age-related pattern is driven by medical recommendations, maternal preferences for controlled birthing experiences, or a combination of both. The rates of C-section deliveries in urban and rural locations varied significantly from one another. These variations are frequently noted among many community groups and districts such variation has been observed in Krishnagiri and Chamrajnagar [ 37 ], and our results concur with those of previous research. The rate of C-section births at tertiary-care hospitals has increased along with improved diagnosis and ease of referral due to the expansion of health care coverage [ 38 ].

Educational attainment is a crucial determinant, with a positive correlation between schooling and the choice of C-section deliveries. Higher education levels appear to influence healthcare decision-making, possibly indicating a greater awareness of medical options and maternal health outcomes. However, the intersection of education, socio-economic status, and access to information warrants further investigation, as these factors can influence women’s autonomy and informed decision-making [ 39 , 40 ]. Furthermore, healthcare facility type emerges as a significant factor, with private hospitals exhibiting a substantially higher prevalence of C-section deliveries. This observation echoes international patterns, wherein private healthcare settings tend to witness elevated C-section rates, attributed to financial incentives and medical practices. This raises questions about the role of medical practices, financial considerations, and patient-provider communication in shaping delivery decisions [ 41 , 42 ]. Of particular interest is the inverse relationship between high-risk fertility behavior and C-section deliveries. This intriguing finding suggests that women classified as high-risk might be directed towards controlled birthing practices, including C-sections, to minimize potential health risks to both mother and child. Mothers with high socioeconomic status, obesity, various pregnancy outcomes, and high-risk birth weight were found to be substantially linked to caesarean sections. Previous research indicates that older moms, even in the absence of problems, are more likely than younger mothers to use healthcare services, experience issues during pregnancy and delivery, and have a C-section birth [ 43 , 44 , 45 , 46 , 47 ]. Consistent with previous research, researchers have discovered that a greater socioeconomic position is positively correlated with C-section rates, contributing to the rich-poor gap. However, further research is warranted to unravel the complexities of this relationship, considering medical indications, patient preferences, and healthcare provider practices [ 48 ]. Socio-economic factors also intertwine with healthcare decisions. Wealth index and urban residence exert substantial influence, with an increase in household wealth correlating with a higher propensity for C-section deliveries. Financial considerations and access to healthcare resources likely contribute to this phenomenon, underscoring disparities in healthcare utilization and raising questions about equitable access to quality care [ 49 ].

The Bayesian multilevel analysis, provides a comprehensive lens to understand C-section deliveries. Individual-level, household-level, and integrated characteristics are considered, reflecting the intricate interplay between personal, familial, and contextual factors. The individual-level model reveals that maternal age at childbirth, schooling, place of delivery, high-risk fertility behavior, and age at marriage significantly influence C-section decisions. These findings emphasize the need for tailored interventions that account for individual medical and demographic attributes. For instance, the positive relationship between age at marriage and C-section deliveries could reflect cultural norms, maternal health considerations, and access to information. Such insights are critical for developing targeted interventions that address diverse needs and preferences [ 35 , 39 ]. Household-level factors, such as wealth index, residence, religion, and social status, all their socio-economic variables affect the preference of C-section. C-section Notably, wealth index exhibits a positive correlation, corroborating the role of financial resources in healthcare choices. Urban residence and religious affiliation emerge as significant factors, further accentuating the role of access, beliefs, and community norms. Policymakers must recognize the intertwined nature of socio-economic and cultural factors, tailoring policies to promote equitable access to quality maternal care [ 41 , 50 ]. Spatial analysis, introduces a geographic dimension to C-section prevalence. Spatial autocorrelation and hotspot analysis, delve deeper into the distribution of C-section deliveries where preference of c section can be seen in urban and richest category. Higher rates of C-section deliveries among urban and wealthier women in India may be influenced by factors such as greater access to medical facilities, a preference for perceived convenience and control, the medicalization of childbirth, fear of pain and complications, and cultural preferences. Educational disparities, social norms, and insurance coverage can also play a role. Efforts to address this trend involve promoting evidence-based practices, educating healthcare providers and the public, and addressing systemic issues in the healthcare system [ 51 ]. These analyses unveil localized influences and clustering patterns, offering insights into regional healthcare practices. High Moran’s I value and significant clusters emphasize that certain variables exhibit strong spatial autocorrelation, reflecting the role of geography and context in healthcare decisions. The majority of India’s southern states have high rates of C-section deliveries. The primary cause of this shift is the rise of institutional deliveries, which is contributing to the trend toward caesarean deliveries in all of the southern states. Urban areas have a greater rate of C-sections in the majority of states. Numerous factors, including sophisticated medical facilities with cutting-edge obstetric treatments, women’s preference for private facilities, high rates of maternal healthcare utilisation, and profit-driven competition, all impact the use of C-sections in urban locations [ 52 , 53 , 54 , 55 , 56 ]. This visualization enhances our understanding of regional disparities, identifying areas with elevated and diminished C-section rates. Policymakers can leverage this information to develop targeted strategies aimed at optimizing maternal and neonatal health outcomes, while minimizing disparities in access. By identifying regions with potential higher prevalence of C-sections, policymakers can work towards balanced and evidence-based healthcare policies [ 50 ]. Policymakers and healthcare providers can collaborate to ensure that spatial patterns do not lead to inequitable healthcare access and outcomes [ 50 ].

This study focuses on C-section prevalence during the years 2019–2021, which may limit its ability to capture long-term trends and changes over time. While the study identifies associations between socio-demographic factors and C-section choices, it does not establish causality. Further research would be needed to explore the causal relationships between these variables. The study’s findings rely on available data sources, which may have limitations in terms of accuracy and completeness. The quality of data can impact the validity of the study’s conclusions. The study focuses on South India, and its findings may not be directly applicable to other regions or countries with different healthcare systems and socio-cultural contexts. In summary, this study offers valuable insights into the factors influencing C-section choices in South India. While it provides a comprehensive analysis, it also has limitations related to data, causality, and generalizability, which should be considered when interpreting its findings and designing future research or policies.

In conclusion, this discussion delves into the multi-faceted landscape of C-section deliveries in South India. The exploration of associations between socio-demographic characteristics and C-section choices underscores the intricate interplay between age, education, healthcare settings, wealth, and urbanization. Bayesian multilevel and spatial analyses offer holistic insights that consider individual, household, and contextual dynamics. These findings hold implications for healthcare policies and interventions. Policymakers and healthcare providers should leverage this knowledge to develop nuanced strategies that ensure optimal maternal and child health outcomes. Recognizing the complexity of explaining differential C-section rates, stakeholders must address healthcare disparities, tailor interventions, and promote evidence-based decision-making. By doing so, South India can move towards more equitable and effective maternal and neonatal healthcare practices.

Data availability

This study was based on a large dataset that is publicly available on DHS website ( https://dhsprogram.com/data/ ) conducted by the MOHFW and International Institute for Population Sciences (IIPS) in India with ethical standards being complied with including informed consent obtained from participants.

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Singh, M., Singh, A. & Gupta, J. Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data. BMC Public Health 24 , 2514 (2024). https://doi.org/10.1186/s12889-024-19984-8

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Public health research in India in the new millennium: a bibliometric analysis

Anuska kalita.

1 Department of Population Health, IKP Trust, New Delhi, India

Sachin Shinde

2 Sangath, Goa, India

Vikram Patel

3 London School of Hygiene and Tropical Medicine, London, United Kingdom

4 Public Health Foundation of India, New Delhi, India

Public health research has gained increasing importance in India's national health policy as the country seeks to address the high burden of disease and its inequitable distribution, and embarks on an ambitious agenda towards universalising health care.

This study aimed at describing the public health research output in India, its focus and distribution, and the actors involved in the research system. It makes recommendations for systematically promoting and strengthening public health research in the country.

The study was a bibliometric analysis of PubMed and IndMed databases for years 2000–2010. The bibliometric data were analysed in terms of biomedical focus based on the Global Burden of Disease, location of research, research institutions, and funding agencies.

A total of 7,893 eligible articles were identified over the 11-year search period. The annual research output increased by 42% between 2000 and 2010. In total, 60.8% of the articles were related to communicable diseases, newborn, maternal, and nutritional causes, comparing favourably with the burden of these causes (39.1%). While the burdens from non-communicable diseases and injuries were 50.2 and 10.7%, respectively, only 31.9 and 7.5% of articles reported research for these conditions. The north-eastern states and the Empowered-Action-Group states of India were the most under-represented for location of research. In total, 67.2% of papers involved international collaborations and 49.2% of these collaborations were with institutions in the UK or USA; 35.4% of the publications involved international funding and 71.2% of funders were located in the UK or USA.

Conclusions

While public health research output in India has increased significantly, there are marked inequities in relation to the burden of disease and the geographic distribution of research. Systematic priority setting, adequate funding, and institutional capacity building are needed to address these inequities.

Although research is increasingly recognised as one of the driving forces behind global health and development, the research output from low- and middle-income countries (LMICs) such as India compares poorly with that of high-income countries ( 1 – 5 ). This phenomenon has been powerfully captured by what the Global Forum for Health Research popularised as the ‘10/90 gap’: the fact that of the over $70 billion spent worldwide on health research each year, only about 10% is invested in research into 90% of the Global Burden of Disease (GBD). This inequity in the global distribution of health research is further compounded by regional inequities, for example, in the biomedical focus of research, and in geographical and population representation. As a result, the knowledge generated by health research does not adequately address the needs of countries and hinders the implementation of evidence-based policy and practice. It is in this context that there are increasing calls for strengthening health research capacity in developing countries as a ‘critical element for achieving health equity’ ( 6 , 7 ).

The public health research situation in India is characteristic of the low priority to public health more generally. A recent review by Dandona et al. ( 8 ) observed that only 3.3% of the 4,876 health research studies published from India during 2002 were devoted to public health. Clearly, public health research in India is grossly under-represented and requires strategic planning, investment, and resource support if there is to be a positive change in the production of such research in the country and, by its application, the promotion of healthier lives for its population ( 9 ). A focus on addressing health inequalities, on evidence-based policy making, on universal health care, and achievement of the Millennium Development Goals are notable public health goals of the new millennium, both globally and in India. In India, public health research has been emphasised as a core investment and tool to guide policy and practice as the country embarks on an ambitious agenda to universalise health care ( 10 , 11 ). The formation of the Department of Health Research is an example of a step by the government in this direction. This is an institution created in 2007 by the Indian government under the Ministry of Health and Family Welfare – which is the central ministry for health in India. The primary mandate of this department is to promote and co-ordinate basic, applied, operational, and clinical research; provide guidance on research governance; promote inter-sectoral and international collaborations; as well as advance training and grants in medical and health research ( 12 ).

It is in this context, that we undertook a systematic situational analysis of public health research in India in the new millennium, with the aim of describing public health research output, whether its focus reflects the current burden of diseases, whether the research is equitably distributed in the country, the research institutions, and funders and collaborations for public health research.

Bibliometric analysis is a method used to describe patterns of publication within a given field or body of literature ( 13 – 15 ). The methodology used in this study parallels other bibliometric studies undertaken to evaluate research production in specific scientific disciplines and/or world regions ( 16 – 18 ). Two data sources were selected: PubMed, an open-access international database of medical journals and IndMed, an open-access database of Indian medical journals. The search strategy was determined by the operational definitions of relevant terms – public health and public health research – which are the focus of this study. Notably captured by Acheson in 1999 and by Last in 2000, several definitions of public health exist, which typically reflect the wide scope of public health itself ( 19 , 20 ). Definitions of both public health [as stated by the World Health Organization (WHO) in 1998] and of public health research (stated by the Strengthening Public Health Research in Europe) accept that the key common points are the population approach (public health) and the production of generalisable knowledge (research) ( 21 , 22 ).

In case of PubMed ( www.ncbi.nlm.nih.gov/pubmed ), an ‘advanced search’ of the title, keywords, and the entire article was conducted with Medical Subject Headings (MeSH), a comprehensive vocabulary for the purpose of indexing journal articles in the life sciences. In the MeSH tree, health care is a ‘major topic,’ which includes public health as a sub-head ( 23 ). Since health care also included articles that were not related to public health, a combination of the two MeSH terms were used.

The search terms used were:

• MeSH major topic – health care + public health , AND
• Text word – India , AND
• Publication date – from 2000/01/01 to 2010/12/31

The search yield was 7,844 references. Selected abstracts were directly imported into an EndNote library. To ensure that all articles related to public health have been included, analyses to test the accuracy of the search terms were conducted for combinations of MeSH major topic health care with MeSH terms diseases, mental disorders, social sciences , and Anthropology , Education , Sociology , and Social Phenomena . For the first accuracy analyses, it was found that all relevant articles were included in the primary search ( healthcare + public health ). For the fourth accuracy analysis, 2,566 articles were found to be relevant to our study but were not included in the original search yield. These were added to make the total PubMed yield 10,410.

IndMed is a database covering peer-reviewed Indian biomedical journals and complements PubMed. It covers 62 journals indexed from the publication year 1985 onwards. After reviewing the ‘advanced search’ option in IndMed with ‘ public health ’ in keywords and the year of publication (individually for each year from 2000 to 2010), we observed that the results were unlikely to be complete. For instance, only 19 abstracts were listed for the year 2000 with this search combination from all journals. Thus, we used a different strategy searching each journal individually. Of the 62 journals, 9 were indexed in PubMed. Of the remaining 53, 17 journals were selected on the basis of table of content analysis revealing at least 5% of the articles per randomly selected set of issues on themes of public health research. The indexing of these 17 journals was incomplete for most journals. To address these gaps, additional searches were conducted. The first strategy involved web-searches of the table of contents from the journal websites (four journals had websites with archives of abstracts). For seven journals, external websites or databases were used to close data gaps. For the remaining six journals, hand searches were conducted in the following libraries – the National Medical Library and the B.B. Dikshit Library at the All India Institute of Medical Sciences, Delhi, and the Dorabji Tata Library at the Tata Institute of Social Sciences, Mumbai.

We screened abstracts of all identified articles from either of these two databases for inclusion for bibliometric analysis. In case of articles that did not have abstracts, the full text was screened. The following inclusion criteria were used:

• Published in English language.
• Must be data-based (either primary and/or secondary).
• Studies must be undertaken in India – either exclusively, or in India as one of the countries in a multi-country/study.

To ensure reliability, two independent reviewers screened each paper and the two EndNote libraries were matched, thus leading to a reliability check of 100% of the selected abstracts. In addition, a randomly selected sample of 500 abstracts from across the 11 years was manually checked by a third reviewer.

Based on the inclusion criteria, 5,869 articles from PubMed and 2,024 articles from IndMed were found to be eligible, yielding a total sample of 7,893 articles. Each abstract (or full-text of papers without abstracts) of the 7,893 eligible papers were reviewed by two independent reviewers and categorised under biomedical disease focused papers or papers that described determinants, policy, and practice. Biomedical disease focused papers were further categorised into three categories based on the GBD Study definitions, viz., GBD 1 included studies on communicable diseases, maternal and neonatal health, and nutritional disorders; GBD 2 included studies on non-communicable diseases and mental and behavioural disorders; and GBD 3 included studies on injuries. Articles that involved research on two or more GBD categories were classified under each of them. The non-disease category included articles on social determinants of health, history of medicine, ethics, policy, and programmatic research that is not related to specific disease burden categories. Abstracts were categorised independently by the two reviewers; discrepancies were addressed by consulting a third reviewer.

To analyse the disease focus and geographical distribution of public health research in India, data were extracted into a spreadsheet for the following parameters from each article 1) disease focus – as per the GBD categories; 2) location of the research study across all states and union territories of India; 3) corresponding author's institution (as a proxy for the research institution leading the study); and 4) location of the corresponding author's institution across all states and union territories of India.

To analyse funding source and international collaborations, we randomly selected 1,600 articles (20% of the total sample) for more detailed analyses of the full manuscript. We also attempted to fill data gaps in any of these categories of information through web-based searches and direct communication with authors. This yielded 1,076 papers with information about collaborations (approximately 67% of the sub-sample, and 13.7% of the total sample), and 870 papers with funding sources (approximately 54% of the sub-sample and 11% of the total sample).

Descriptive analysis and frequencies were used to describe absolute outputs over time, examine outputs in different categories of GBD over time, geographical distribution of research/research institutions, collaborations, and funders.

Ethics statement

The study was reviewed and has been approved by the Institutional Review Board of Sangath (Sangath-IRB).

Absolute research output

The total number of eligible articles included in the bibliometric analysis from both PubMed and IndMed was 7,893 (5,869 from PubMed and 2,024 from IndMed). The process of data collection is shown in Fig. 1 . There was a trend of an increase in publication over time, with the total number of publications in 2010 ( n = 817) showing a 72.3% increase compared with 2000 ( n =474). Figure 2 shows the trend of published research output over the decade. Although there was an overall increase in the number of publications between 2000 and 2010, the number declined sharply between 2007 and 2009. Specific reasons for this decline were not detected.

The process of data collection for bibliometric analysis.

Absolute research output from India during the decade 2000–2010.

Distribution of public health research

Out of the 7,893 papers, 6,103 reported the topic of research as one or more of the GBD conditions. We observed that the majority of the papers with a biomedical focus were related to conditions in the GBD 1 category across all 11 years (60.8%, 3,711/6,103), compared with a burden of disease, as estimated at the mid-point of the decade in 2004, of 39.1% ( Fig. 3 ). The proportion of lost DALYs (Disability Adjusted Life Years) caused by conditions under GBD 2 category for India was 50.2% in 2004. Compared to this burden, only 31.7% (1,933/6,103) publications focused on conditions under this category. The proportion of research focused on diseases in GBD 3 is 7.5% (458 out of 6,103), which is slightly lower than the burden of disease in this category (10.7%) in India.

Publication research focus relative to the burden of disease in India during 2000–2010.

Note : Burden of disease (DALYs) for GBD categories are estimates for the year 2004.

We observed a trend of reduced proportion of GBD 1 and a proportionate increase in those related to GBD 2 over time, although the proportionate distribution of research in the later years still does not match the burden of disease reported in the GBD 2010 ( Fig. 4 ).

Trends in publications from India by global burden of disease categories from 2000 to 2010.

The geographical equity in public health research output is skewed. For this, we considered the Empowered Action Group (EAG) that was constituted by the Ministry of Health and Family Welfare in 2001 to facilitate area-specific interventions for the eight most populous and poorest states (viz. Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan, Orissa, Uttarakhand and Uttar Pradesh), which together account for 45.9% of India's population and 56.5% of the poor were the location of just 10% of publications (801/7,893) ( 24 ). This is presented in Fig. 5 .

Per capita distribution of research studies in India.

The research actors

Out of our total sample of 7,893 papers, 7,706 papers reported corresponding addresses. From this sample, 78.4% (6,044/7,706) reported an Indian research institution. In total, 42.5% (2,572/6,044) of the papers were produced from research institutions located in just three states of Delhi, Maharashtra, and Tamil Nadu. Table 1 lists the 15 leading research institutions in India. Together these institutions produced 21% (1,258/6,044) of the research papers from India during the last decade; the majority of these institutions were located in Delhi and Maharashtra. Another observation was the disparity in production of research even among these top 15 institutions, which ranged from a maximum of 555 papers to a minimum of 13. The north-eastern seven states accounted for the least number of research institutions (1.4%, 111/7,706), while the eight EAG states accounted for 12.7% (979/7,706) of research institutions.

The 15 leading institutions for public health research in India

Of the 7,706 publications that reported a corresponding author institution, 21.5% (1,662/7,706) were foreign. Based on full-text analyses of the randomly selected sub-sample, a further 19.6% (210/1,076) of papers with first author affiliation to an Indian institution reported foreign collaborators. These 210 papers mentioned a total of 275 different international collaborators. Of the foreign corresponding author institutions, a majority – 65% (1,078/1,662) were from two countries – the United States of America and the United Kingdom. A similar proportion (57.6%) was observed for other foreign collaborators, that is, excluding corresponding author institutions. The leading foreign institutions undertaking public health research in India are shown in Table 2 . Together, these institutions led 26.9% (442/1,662) of the papers and were involved in collaborations on 89% (187/210) of the papers.

The 10 leading international collaborating institutions for public health research in India

Eight hundred and seventy papers of the sub-sample of 1,600 papers yielded information on funding sources. In total, 34.1% (297/870) listed an Indian funding agency and the remaining two-thirds (573/870) listed a foreign funding source. The main funding institutions supporting public health research in India are listed in Table 3 . In total, 81.5% (709/870) of papers were funded by these 10 agencies. While all the four Indian funders are governmental institutions, international funding agencies represent a mix of multilateral and bilateral organisations (WHO and the Department for International Development-UK) and private foundations (Wellcome Trust and the Bill and Melinda Gates Foundation).

The 10 leading funders of public health research in India

This paper describes the results of an analysis of public health research in India in the new millennium. The data source was a bibliometric analysis of one of the largest international and the largest national databases of medical research. Our main findings were that while public health research output has increased substantially over the course of the first decade of the new millennium, there is considerable maldistribution of research in terms of the disease focus and the geographical focus. Most research is funded by international donors with relatively low levels of domestic public or private sector investment. International academic partners, particularly from the USA and the UK, play influential roles in research with little evidence of south–south partnerships with other developing countries.

In a country which bears a disproportionate amount of the GBD, it was reassuring to observe that the total number of publications based on public health research in India has substantially increased over the first decade of the millennium; however, this increase (of 72.3%) falls well below that of other middle-income countries such as South Africa (225% increase from 2000 to 2010) ( 25 , 26 ), Mexico (102% from 1995 to 2004) ( 27 ), and Brazil (241% increase from 1995 to 2004) ( 28 ). This absolute increase in the volume of publication masks striking inequities both in terms of the research focus and the research settings. Even according to the recent GBD estimates of 2010, while GBD 2 and 3 conditions accounted for 45 and 12% (together 57%) of the burden of disease, just 35 and 7% (42%) of papers focused on these conditions ( 29 ). These findings are consistent with the only other bibliometric study from India and those from other LMICs ( 2 – 5 , 30 ). This skewed picture has been attributed to the misconceived notion of research agencies and donors regarding the association of these diseases with affluence ( 27 , 31 – 34 ) even though the majority of GBD 2 and 3 conditions are more frequent among poorer populations in LMICs ( 27 , 35 – 40 ).

In addition to the under-representation of research on leading causes of the burden of disease in India, there is a markedly inequitable representation of vulnerable contexts or population groups in India. Capacities exist, but are unequally distributed, as is evident from the concentration of research institutions in richer states of the country such as Delhi, Maharashtra, West Bengal, and Tamil Nadu. A number of factors contribute to these maldistributions – dependence on foreign funding and donor-driven research priorities, asymmetries in capacities of researchers and institutions leading to a concentration of research in a few subject areas and geographies, and a policy and research-system vacuum. The lack of research institutions in states contributing to the highest proportions of poverty and disease burden in the country potentially contributes to a vicious cycle of low capacity to carry out public health research, which is relevant to these populations.

International institutions, both donors and research partners, play a leading role in public health research in the country. Two-thirds of the publications were based on research funded by foreign donors. This compares unfavourably with other middle-income countries such as Brazil and China where 74.3 and 78.6% of the total health research funding comes from the domestic public sector agencies and only 2.2 and 8.8% comes from international funding agencies ( 41 – 44 ). This reliance on international funding may contribute to the inequities in the distribution of research, such as an undue focus on international goals like the MDGs. These issues of skewed priorities and funding need to be addressed through a significant increase in domestic investments in public health research that is transparent, accountable, and responsive to the burden of disease and the needs of diverse geographical regions and populations of the country. There is also a need for domestic private philanthropies to support public health research; in Brazil, for example, domestic private sector organisations contribute 23.3% investments in public health research ( 43 ). Channelling private-sector support towards public health research assumes special relevance in the context of the recent Companies Bill that mandates 2% allocation of profits of listed companies towards corporate social responsibility ( 45 ).

Given the inequitable distribution of research institutions and focus areas in the country, the focus of capacity strengthening efforts to build institutions, especially in resource-poor states and in neglected public health focus areas is urgent. However, attracting and retaining researchers within institutions require coordinated strategies that address familiar barriers such as the lack of academic liberty, absence of professional incentives, poor and non-transparent funding, bureaucratic obstacles, and unclear career pathways ( 9 ). The weak public health research environment in India needs strengthening through a comprehensive approach. There is often little communication and consultation between the producers of research and the users of research: policy-makers, health providers, civil society, the private sector, other researchers, and the general public. It is important to recognise that the health research process spans the entire spectrum of policies related to knowledge creation as well as its diffusion and use. Therefore, a well-coordinated, systematic approach to health research needs to involve all stakeholders. For instance, priority setting needs to underlie the efforts to increase the quality, relevance, and production of research by considering whether there is a demand for this research. The paucity of forums to interact and share knowledge, inaccessibility of existing global resources and information asymmetry, and the lack of systematic dissemination of research towards policy and practice all lead to a weak research ecosystem.

Collaborations between domestic, as well as international researchers and institutions, can foster such exchange and access. Evidence from South Africa and Brazil suggests that international collaborations dramatically boost the volume of health research publications in high impact peer-reviewed journals ( 46 , 47 ). To realise the potential of collaborative research, it is crucial that local capacities are strengthened and relationships between domestic and international institutions are based on equal partnerships. An issue of note here is the dominance of the USA and the UK in collaboration for public health research in India. South–south collaborations, either with countries such as Brazil or South Africa with vibrant public health research cultures, or with other countries in South Asia which share similar public health priorities, were negligible. Steps need to be built on to encourage cooperation, such as – facilitating discussions and sharing of national experiences; supporting cross border training; developing networks of researchers, policymakers, and institutions; and increasing political visibility of health ( 48 – 50 ).

The weakness of governance systems that regulate and monitor public health research in the country often lead to insufficient coordination. Research activities in various health-related fields have been fragmented, isolated from each other, and wastefully duplicative. In a context like India, where both financial and human resources are scarce, this is inefficient and sub-optimal. While the Department of Health Research was set up under the Ministry of Health and Family Welfare by the Government of India in 2009–2010 ( 12 ), a policy for health research, a clear mandate and empowerment of the Department, and systems of convergence with existing departments and government institutions have yet to clearly articulated. The current need in India is for the health research system to identify priorities, mobilise resources, both public and private, and maximise the use of existing ones, develop and sustain the human and institutional capacity necessary to conduct research, disseminate research results to target audiences, apply research results in policy and practice, and evaluate the impact of research on health outcomes. Good quality research can and must be generated to continuously address critical knowledge and practice gaps to advance innovation in and improve implementation of public health programmes. Such research cannot be viewed as an indulgence in resource-poor states but needs to be at its most creative and relevant in precisely those contexts.

The last decade has seen some positive developments in the area of health. Recommendations for universalisation of health coverage ( 10 ) increased investments in health in the 12th Five-Year Plan period ( 11 ), and the proposal for a comprehensive and convergent National Health Mission ( 11 ) is all desirable goals, which need evidence generation for their effective implementation. Public health research priorities and investments need to be convergent with, and not parallel to, these goals.

This study suffers from the typical limitations of bibliometric analyses, that is, the fact that these miss out on articles or journals, which are not indexed. Another limitation could be the risk of misclassification of articles (in particular regarding focus areas) despite our robust efforts to minimise this bias. Additionally, newer articles published from 2011 till date have not been included within the scope of this study, and we acknowledge that there might be changes in the trends of public health research in India in the last 4 years. Nevertheless, our findings represent the most comprehensive analysis of public health research in India in the current millennium and serve as a reference for the evaluation of future research production metrics.

While public health research output in India has increased significantly in the first decade of this millennium, there are marked inequities in relation to the burden of disease and the geographic distribution of research. Systematic priority setting, adequate funding, and institutional capacity building are needed to address these inequities. It is imperative that India invests adequately in developing a vibrant and rigorous ecosystem of public health research at the heart of its public health strategy.

Acknowledgements

We thank all the respondents of the in-depth interviews whose invaluable insights about public health in India added immensely to the study. We thank all the individuals who have contributed to this study at different stages. We specifically acknowledge Archana Patil, Caetano Parras, Kishori Mandrekar, Melba Pinto, Pranjali Rodrigues, and Swamini Kakodkar who gave their valuable support in data cleaning and extraction; Smita Naik who designed the data extraction software and formats; Gracy Andrew who lent her ideas in the initial phases of the study; and Dr. Shinjini Mondal who was involved in data collection, data review, and bibliometric analysis. Above all, we express our deep gratitude to our funders – the ICICI Foundation for Inclusive Growth (IFIG) for their continuous support and understanding. VP is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science.

Authors' contributions

VP and AK conceived the study. VP provided overall guidance. AK led the bibliometric analysis and SS led the stakeholder analysis. AK prepared the first draft. VP, AK and SS finalized the draft.

Conflict of interest and funding

We declare that we have no conflicts of interest. Funding for the study was received from ICICI Foundation for Inclusive Growth (IFIG), Mumbai. VP is supported by a Wellcome Trust Senior Research Fellowship. AK led the Centre for Child Health and Nutrition (an organization funded by IFIG) before of the completion of the study.