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Full Research Article

Government Health Expenditure and Sustainable Food Consumption: A State-Level Efficiency Analysis of Child Survival in India

V
Vivek Babbar1,2,*
J
Jitender Bhandari1
1Economics, Christ (Deemed to be) University, Bangalore-560 029, Karnataka, India.
2Zakir Husain Delhi College (Evening), University of Delhi, Delhi-110 002, India.

ABSTRACT

Background: The Central Government of India adopted the National Rural Health Mission (NRHM) in 2005, the Sustainable Development Goals (SDGs) in 2015, the National Health Policy (NHP) in 2017 and the POSHAN program for enhanced food nutrition in an attempt to increase Government Health Expenditure (GHE) and upgrade state-level health facilities.

Methods: This paper analysis the efficiency of the GHE on child survival outcomes comparing National family health survey rounds 4 and 5 using Stochastic Frontier Analysis (SFA) and also to measure the state-wise efficiency of GHE on health outcomes.

Result: The findings indicate that there is a steady increase in TE and a steady decrease in the technical inefficiency in all three outcomes in NFHS-5 compared to NFHS-4. Government health spending is found to be a statistically significant of child survival in both survey rounds and the elasticity of government health spending in NFHS-5 was higher. Conversely, the health centers per lakh population do not have a statistically significant independent effect. The negative relationship between female literacy, as a determinant of inefficiency, is statistically insignificant, which means that the current efficiency gains are the result of the enhanced performance of the health systems and not of the socio-demographic variations between states. The results highlight the effectiveness of efficiency-based health policies and the continued allocation of funds, such as increased state expenditure on nutritional foods and dairy products, to further enhance child survival rates in India.

INTRODUCTION

Government Health Expenditure (GHE) is an important ingredient of the government budget. According to the National Health Account Report, Fig 1 shows that India’s GHE increased significantly from 1.13% of GDP in 2014–15 to 1.84% in 2021-22, but is still short of the 2.5% GDP target. The government’s major attempts to achieve financial stability and Universal Health Coverage (UHC) for its residents are supported by the consistent rise in overall health spending. Even throughout the COVID-19 pandemic, the government prioritised primary care (55.9% of GHE), with large programs like Ayushman Bharat driving investment. Described as the largest government-funded health assurance program in the world, it aims to give over 10-12 crore impoverished and vulnerable families health coverage of ₹ 5 lakh per family annually for secondary and tertiary care hospitalisation. Studies by Deaton and Dreze (2009); Misra et al., (2011); George et al., (2024); Zadgaonkar (2025) and Kumar et al., (2025), further highlighted the significance of dietary intake of nutritious foods and dairy products in improving health outcomes in India. In response to combat malnutrition among children, expectant mothers, nursing mothers and teenage females, the Indian government launched the POSHAN Abhiyaan in 2018.

Fig 1: The percentage of GDP (gross domestic product) allocated to government health spending (GHE).


       
As a result, out-of-pocket (OOP) spending, which is a defining characteristic of private health expenditures in India, is declining to its proportion of Total Health Expenditure (THE) from over 62.6% in 2014-15 to roughly 47.1% in 2020-21 due to increased public spending. Significant interstate disparities are highlighted by National Family Health Survey (NFHS) data. For example, in Table 1 Kerala (KR) has an infant mortality rate (IMR) of about 4.4 per 1,000 live births, but states like Uttar Pradesh (UP) have an IMR of 50.4 per 1,000 live births. As presented in Table 1, these persistent regional disparities are a result of large and economically weaker states like Uttar Pradesh (UP), Bihar (BR), Madhya Pradesh (MP) and Chhattisgarh (CH) continuously falling behind better-performing southern states. In spite of these differences, NFHS-5 outperforms NFHS-4 in terms of living conditions, access to healthcare, child survival and fertility reduction. Improvements in results, however, may not always indicate effective resource use. When resources are used to produce the best possible health results, public spending is considered efficient. According to recent studies by Tigga and Sarkar (2025), inefficiencies in health expenditures rose from 17% in 2014-15 to 26% in 2019-20, indicating that higher spending is not enough on its own without improvements in resource allocation, governance and service delivery. Both nationally and internationally, a significant amount of research has been done on the efficiency and productivity of health systems. Studies on OECD countries (Afonso and Aubyn, 2011; Cetin and Bahçe, 2016; Samut and Cafrý, 2015), Sub-Saharan Africa (Grigoli and Kapsoli, 2018; Kinfu, 2013), the Eastern Mediterranean region (Masri and Asbu, 2018), ASEAN countries (Singh et al., 2021) and India (Acharya et al., 2019; Purohit, 2014; Mohanty and Bhanumurthy, 2020; Sarma and Kamble, 2017). Overall, the literature review assesses the effectiveness of GHE on health outcomes; however, no research has been conducted in India to examine the effectiveness of GHE with respect to health outcomes between NFHS 4 and NFHS 5 rounds. Between these two cycles, Significant policy changes, such as the adoption of the Ministry of Health and Family Welfare (2017), Ministry of Health and Family Welfare (2018)  and alignment with the Sustainable Development Goals (SDGs) in 2015. India had gains in important health indices and a rise in public health spending throughout this period, although regional inequities persisted. This situation offers a useful chance to determine whether higher spending has resulted in better efficiency. So the first objective is to compare the efficiency of GHE on health outcomes using two NFHS rounds and the second is to measure the state-wise efficiency of GHE on health outcomes.

Table 1: States disparity in terms of health indicators (NFHS-5). Source: NFHS-5.


 
Role of sustainable food consumption and dairy product on health outcome
 
By lowering the risk of chronic, non-communicable diseases, sustainable food consumption which prioritizes nutrient-dense, locally sourced and ecologically friendly choices significantly improves health outcomes. As a “nutrient-dense powerhouse” that supplies vital nutrients (calcium, high-quality protein) required for health, dairy products play a complicated role in this. Dairy consumption, especially milk consumption, is typically linked to better development and health outcomes in children and adolescents, according to evidence from systematic reviews (Akyil et al., 2026 ; Zhang et al., 2021). The positive relationship between dairy consumption and metabolic health is further supported by narrative evidence (Timon et al., 2020). Increased consumption of fruits and vegetables is associated with increased life satisfaction and well-being, broader dietary patterns are also important (Holder, 2019). On the other hand, particularly in the least developed nations, rising food costs can exacerbate under nutrition and raise infant and child mortality (Lee et al., 2016; An, 2013). All things considered, sustainable diets greatly improve health outcomes.
       
The rest of the study, Section 2, describes the study’s materials and methods, while Section 3 presents the result and discussion and Section 4 provides the conclusion and policy implications. 

MATERIALS AND METHODS

Twenty states were included in this study: Rajasthan (RJ), Assam (AS), Tamil Nadu (TN), BR, Gujarat (GJ), Haryana (HR), Himachal Pradesh (HP), Karnataka (KN), KR, MP, Maharashtra (MH), Odisha (OD), Punjab (PN) andhra Pradesh (AP), UP, JK, Jharkhand (JH), CH, Telangana (TN) and Uttarakhand (UK). These states, which together account for about 85.91% of India’s GDP, were chosen to guarantee both geographic representation and economic significance.
       
In order to compare efficiency over time, the analysis is based on cross-sectional secondary data from two rounds of the NFHS-4, 2015-16 and NFHS-5, 2019-20. The study conceptualizes health systems as production units that transform inputs into desirable health outcomes. The primary input variable is Per Capita GHE(PCGHE) made by all government agencies, quasi-governmental organizations and donors in the event that funds are transferred through government organizations are all included in a State or UT’s GHE. The number of total health centers per lakh population (PCTHC), a control variable that represents health infrastructure, is also included in the study. After accounting for population size, this metric combines sub-centers (SCs), primary health centres (PHCs) and community health centres (CHCs), which are sourced from Rural Health Statistics, Statistics Division, Ministry of Health and Family Welfare. According to Filmer and Pritchett, 1999 and Bloom et al., (2004), government health spending by itself does not improve mortality indicators unless it is converted into actual service delivery by the entire health centre. The study also includes the inefficiency, female literacy variables, which affect the health outcome. Women’s illiteracy is less likely to Identify warning indicators of pregnancy and children’s illnesses and employ preventative measures, such as nutrition, cleanliness and immunization (Kinfu, 2013). Female literacy rate is sourced from the NFHS, MoHFW. Per capita values were calculated by the estimated population of the states for each year to obtain per capita values sourced from the National Commission on Population Projections.
       
So the present study covers the mortality health indicators, IMR, NMR and Under-five Mortality Rate (U5MR). But we derived the survival rate from these health indicators because in SFA, the required outputs were “more is better.” The authors transform IMR, NNM and U5MR into infant survival rate (ISR), neo natal survival rate (NSR) and under five survival rate (U5SR), calculated as:
 
 
        
These indicators reflect the number of surviving children per 1,000 live births and serve as appropriate output variables in the efficiency framework. Data from these indicators are sourced from the NFHS, MoHFW.
       
The study uses Stochastic Frontier Analysis (SFA), a popular econometric method for assessing technological efficiency in manufacturing processes, to estimate efficiency (Aigner et al., 1977; Meeusen and van den Broeck, 1977; Coelli et al., 2005). The stochastic production frontier log-linear model equation used in this study is similar to the one proposed by Battese and Coelli (1995).
 
ln qi = β0 + Σ βn ln Xni + Vi - Ui    ...(1)      
 
The following efficiency equations are included in this article:
 
lnISR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui           ...(2)
 
lnNSR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui          ...(3)
 
lnU5SR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui          ...(4)
 
Xni =   Input vector which is PCGHE and PCTHC that are associated with state i.
qi = Health output of state i (ISR, NSR and U5SR).
β = Vector of parameters to be estimated.
       
There are two error term components in the model Ui and Vi, respectively, stand for technical inefficiency and the random component, which represents the impact of stochastic events outside the production unit’s control. For the two components of the error terms, four distinct distributional assumptions are made in the literature. This article used the half-normal distribution is the most widely assumed distribution and thus it is also the one employed here (Jondrow et al., 1982).
       
Techincal Efficiency (TE) measures how well each production unit represented by a state in our analysis performs in proportion to its highest potential, given the resources now available to it. Theoretically, the TE score ranges from 0 (the least efficient) to 1 (the most efficient). The logarithmic form of the equation makes it easier to quantify elasticity and to interpret coefficient values, which represent percentage changes in one variable in relation to others.
       
Following the Battese and Coelli (1995) one stage for inefficiency effects model, technical inefficiency is modeled as a function of state-level characteristics:
 
Ui = Zi δ + Wi
 
Zi = A vector of explanatory variables for inefficiency,
δ = A vector of parameters.
Wi = A random error term.
       
In this article, the inefficiency equation includes the following variables:
Ui = δ0 + δ2(lnFL)i + Wi
 
lnFL= Logarithm of female literacy, which is the percentage of women 15 years of age or older who have never attended school. 

RESULTS AND DISCUSSION

Comparison between NFHS 4 and NFHS 5 Round
 
Government health expenditures (GHE) had a positive and statistically significant impact on child survival outcomes in India, according to stochastic frontier analysis utilizing NFHS-4 and NFHS-5 data.  As shown in Table 2 and Fig 2, a 1% increase in per capita government health expenditure on a average elasticity for infant survival rate (ISR) rose from 0.738 (p<0.01) in NFHS-4 to 0.776 (p<0.05) in NFHS-5. Neonatal survival rate (NSR) elasticity increased from 0.671 (p<0.01) in NFHS-4 to 0.785 (p<0.05) in NFHS-5. The equivalent elasticity for the under-five survival rate (U5SR) rose from 0.715 (p<0.01) to 0.814 (p<0.05), keeping other variables constant. The larger elasticity estimates observed in NFHS-5 as compare to NFHS-4 it shows that improved nutrition and sustainable food consumption initiatives supported by public health programs may contribute to the beneficial effects of government health spending on child survival outcomes. On the other hand, in Table 2 shows that the number of health centers per lakh population is still statistically insignificant on any of the survival indicators in both rounds. The inclusion of female literacy as a driver of inefficiency is not statistically significant on all child survival rate, suggesting that its impact on efficiency increases is minimal. According to the Table 2, between NFHS-4 and NFHS-5, the mean TE for Infant Survival Rate (ISR) rose from 0.894 to 0.931, a 3.7 percentage point improvement. Similarly, the mean TE for Under-Five Survival Rate (U5SR) climbed from 0.905 to 0.933 and the mean TE for Neonatal Survival Rate (NSR) increased from 0.906 to 0.936. The higher technical efficiency scores observed in NFHS-5 of all indicators.

Table 2: Comparative child health outcomes stochastic frontier results (NFHS-4 vs. NFHS-5).



Fig 2: Differences between NFHS-4 and NFHS-5 in the elasticity coefficients of government health spending for infant survival rate (ISR), neonatal survival rate (NSR) and under-five survival rate (U5SR).


 
Technical efficiency (TE) comparison by states between NFHS-4 and NFHS-5
 
The state-level mean Technical Efficiency (TE) scores for Infant Survival Rate (ISR), Neonatal Survival Rate (NSR) and Under-Five Survival Rate (U5SR) in NFHS-4 and NFHS-5 are shown in Table 3. The mean TE increased from 0.894 to 0.931 for ISR, from 0.906 to 0.936 for NSR and from 0.905 to 0.933 for U5SR between the two survey rounds. As illustrated in Fig 3, Indian states achieved higher levels of technical efficiency during NFHS-5.While Kerala consistently recorded the highest efficiency scores across all indicators followed by Tamil Nadu, Maharashtra and Himachal Pradesh stayed close to the efficiency frontier, In contrast, Bihar recorded the lowest mean TE scores in NFHS-5, with values of 0.875, 0.881 and 0.879 for ISR, NSR and U5SR, followed by Rajasthan, Madhya Pradesh and Jharkhand continued to lag. Interestingly, Madhya Pradesh and Rajasthan showed the most gains in mean efficiency across child survival outcomes, suggesting better use of resources. Despite overall progress, persistent interstate disparities highlight variation in governance, healthcare delivery and health-system performance.

Table 3: State-level scores of TE of infant, neonatal and under-5 survival outcomes of the NFHS-4 and NFHS-5 rounds.



Fig 3: The top five and lowest five states in NFHS-5 according to average of mean technical efficiency (mean of ISR, NSR and U5SR).


       
Government health spending is a positive and statistically significant predictor of child survival in both round which is similar to findings by earlier studies such as Mohanty and Bhanumurthy (2020); Tigga and Sarkar (2025). The result indicating that government spending can promote health through nutrition-support programs in addition to healthcare services. Initiatives like POSHAN Abhiyaan, Integrated Child Development Services (ICDS), Mid-Day Meal Scheme, Anaemia Mukt Bharat and supplemental nutrition programs encourage mothers and children in India to have access to wholesome, balanced diets. Food security and improved dietary quality are key elements of sustainable food consumption and are well known factors that affect children’s nutritional status and survival (Johnston et al., 2014; Vandana and Khetarpaul, 2017). Therefore, the indirect advantages of nutrition-sensitive interventions funded by public resources partially responsible for the observed improvement in child survival outcomes linked to increased government health. The result also shows higher efficiency observed in NFHS-5 its due to the better targeting, better service delivery and complementarities with socioeconomic characteristics could be the reasons for this increase.  Nevertheless, it appears that in both rounds the number of health centres per lakh population does not have a statistically significant influence, indicating that infrastructure expansion on its own is insufficient in the absence of improvements in service quality and utilization. The higher mean TE scores observed in NFHS-5 as compare to NFHS-4 suggest a more effective utilization of available health resources and a better conversion of government health expenditure into improved child survival outcomes and this indicates that the Indian states have been slowly approaching the boundary of best-practice production of health. There is a negative but negligible correlation between female literacy and inefficiency, suggesting that changes in the health system have contributed more to recent efficiency advances than sociodemographic factors. State-wise variation is also observed, Bihar, Madhya Pradesh and Jharkhand fall short of the efficiency frontier, while Kerala, Tamil Nadu, Maharashtra and Himachal Pradesh continuously perform close to it, similar efficiency variation observed in Afonso and Aubyn (2005), Dutu and Sicari (2016) and Mohanty and Bhanumurthy (2020) who found wide disparities in efficiency levels across countries and states. This disparity is due to structural and institutional limitations are shown by persistent interstate disparities.

FUTURE RESEARCH DIRECTIONS

Due to data limitations, future studies should directly measure dietary diversity, food security, nutritional intake and sustainable consumption pattern in order to assess their impact on child health outcomes and the effectiveness of the health system. In order to investigate the long-term association between government health spending and child health outcomes, the current analysis should be expanded by using panel data methodologies. Deeper understanding of interstate efficiency disparities may be obtained by incorporating indices of service delivery, healthcare accessibility and government quality. Localized inequities that are not represented at the state level may be further identified by district-level analysis.

CONCLUSION

Using Stochastic Frontier Analysis (SFA) based on NFHS-4 and NFHS-5 data, this study investigates the effectiveness of Government Health Expenditure (GHE) in enhancing child survival outcomes across Indian states. Across child, neonatal and under-five survival metrics, the results demonstrate decreased inefficiency and increased technical efficiency with time. GHE shows a statistically significant and favourable impact, with stronger effects in NFHS-5, suggesting improved efficacy through improved service quality and usage. Although female literacy exhibits the anticipated negative correlation with inefficiency, it is not statistically significant, indicating that sociodemographic differences have little impact. From a policy perspective, the results highlight the importance of sustained and targeted public investment in maternal and child health programs, particularly nutrition-focused initiatives such as POSHAN, as literature studies also show the impact of sustainable food consumption and dairy products on health outcomes. Furthermore, the decrease in inefficiency emphasizes how crucial better management, governance and efficient use of current resources are to attaining health benefits.

ACKNOWLEDGEMENT

The authors would like to sincerely thank all institutions and organizations that provided access to the secondary data used in this study. These databases were critical for conducting this analysis. This study received no specific funding.

CONFLICT OF INTEREST

No conflict of interest for this study.

REFERENCES


  1. Acharya, D., Sahoo, B.K. and Venkatachalapathy, T.K. (2019). Health System and Health Expenditure Productivity Changes in Indian States: Has It Changed for the Better in the Post-Reform Period?. In Disease, Human Health and Regional Growth and Development in Asia. Singapore: Springer Singapore. (pp. 71-87).

  2. Afonso, A. and Aubyn, M.S. (2011). Assessing health efficiency across countries with a two-step and bootstrap analysis. Applied Economics Letters. 18(15): 1427-1430. https:// doi.org/10.1080/13504851.2010.54114.

  3. Afonso, A. and Aubyn, M.S. (2005). Non-parametric approaches to education and health expenditure efficiency in OECD countries. Journal of Applied Economics. 8(2): 227-246. https://doi.org/10.1080/15140326.2005.12040626.

  4. Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics. 6: 21-37.

  5. Akyil, S., Winkler, S., Meyer, D., Kiesswetter, E., Kussmann, M., Schwingshackl, L. and Hauner, H. (2026). Association between dairy intake and multiple health outcomes: A scoping review of systematic reviews and meta-analyses. European Journal of Clinical Nutrition. 80(1): 16-27.

  6. An, R. (2013). Effectiveness of subsidies in promoting healthy food purchases and consumption: A review of field experiments. Public Health Nutrition. 16(7): 1215-1228.

  7. Battese, G.E. and Coelli, T.J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics. 20(2): 325-332.

  8. Bloom, D.E., Canning, D. and Sevilla, J. (2004). The effect of health on economic growth: A production function approach. World Development. 32(1): 1-13.

  9. Cetin, V.R. and Bahce, S. (2016). Measuring the efficiency of health systems of OECD countries by data envelopment analysis. Applied Economics. 48(37): 3497-3507. https://doi.org/ 10.1080/00036846.2016.1139682.  

  10. Coelli, T.J., Prasada Rao, D.S., O’donnell, C.J. and Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis. Boston, MA: Springer US.

  11. Deaton, A. and Drèze, J. (2009). Food and nutrition in India: Facts and interpretations. Economic and Political Weekly. 44(7): 42-65. https://www.jstor.org/stable/40278509.

  12. Dutu, R. and Sicari, P. (2016). Public Spending Efficiency in the OECD: Benchmarking Health Care, Education and General Administration. OECD Economics Department Working Papers No. 1278. OECD Publishing. https://doi.org/ 10.1787/5jm3stxq46tg-en

  13. Filmer, D. and Pritchett, L. (1999). The impact of public spending on health: Does money matter?. Social Science and Medicine. 49(10): 1309-1323. 

  14. George, F., Rajeev, L.N., Bandyopadhyay, S., Baby, J., Sinha, S., Sachdev, H.S. and Thomas, T. (2024). The role of dairy consumption in the relationship between wealth and early life physical growth in India: Evidence from multiple national surveys. BMC Public Health. 24(1): 96.

  15. Grigoli, F. and Kapsoli, J. (2018). Waste not, want not: The efficiency of health expenditure in emerging and developing economies. Review of Development Economics. 22(1): 384-403.

  16. Holder, M.D. (2019). The contribution of food consumption to well- being. Annals of Nutrition and Metabolism. 74(Suppl. 2): 44-52.

  17. Johnston, J.L., Fanzo, J.C. and Cogill, B. (2014). Understanding sustainable diets: A descriptive analysis of the determinants and processes that influence diets and their impact on health, food security and environmental sustainability. Advances in Nutrition. 5(4): 418-429.

  18. Jondrow, J., Materov, I., Lovell, K. and Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics. 19: 233-238.

  19. Kinfu, Y. (2013). The efficiency of the health system in South Africa: Evidence from SFA. Applied Economics. 45(8): 1003- 1010. https: //doi.org/10.1080/00036846.2011.613787.

  20. Kumar, V., Akanksha, Singh, S., Yadav, A.K., Kumar, S. and Rathaur, A. (2025). The health benefits of ghee: A comparative analysis of Ayurvedic and modern scientific perspectives: A review. Asian Journal of Dairy and Food Research. 44(6): 890-898. doi: 10.18805/ajdfr.DR-2227.

  21. Lee, H.H., Lee, S.A., Lim, J.Y. and Park, C.Y. (2016). Effects of food price inflation on infant and child mortality in developing countries. The European Journal of Health Economics. 17(5): 535-551.

  22. Ministry of Health and Family Welfare. (2017). National Health Policy 2017. Government of India.

  23. ​Ministry of Health and Family Welfare. (2018). Ayushman Bharat: Comprehensive Primary Health Care through Health and Wellness Centres-Operational Guidelines. Government of India.

  24. Masri, M.D. and Asbu, E.Z. (2018). Productivity change of national health systems in the WHO Eastern Mediterranean Region: Application of DEA-based Malmquist productivity index. Global Health Research and Policy. 3(1): 22. https:// doi.org/10.1186/s41256-018- 0077-8.  

  25. Meeusen, W. and van der Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review. 18: 435-444.

  26. Misra, A., Singhal, N., Sivakumar, B., Bhagat, N., Jaiswal, A. and Khurana, L. (2011). Nutrition transition in India: Secular trends in dietary intake and their relationship to diet related non communicable diseases. Journal of Diabetes. 3(4): 278-292.

  27. Mohanty, R.K. and Bhanumurthy, N.R. (2021). Assessing public expenditure efficiency at the subnational level in India: Does governance matter?. Journal of Public Affairs. 21(2): e2173. https://doi.org/10.1016/j.asieco.2020.101224.

  28. Purohit, B.C. (2014). Efficiency of social sector expenditure in India: A case of health and education in selected Indian states. Healthcare in Low-Resource Settings. 2(1). https:// doi.org/10.4081/hls.2014.1866.

  29. Samut, P.K. and Cafrý, R. (2015). Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel Tobit. Social Indicators Research. 129(1): 113-132. https:// doi.org/10.1007/s11205-015-1094-3.  

  30. Sarma, J.V.M. and Kamble, P. (2017). Efficiency and Adequacy of Public Health System in Improving Health Outcomes: A SFA for Indian States. In Challenges and Issues in Indian Fiscal Federalism. Singapore: Springer Singapore. (pp. 143-158).

  31. Singh, S., Bala, M.M., Kumar, N. and Janor, H. (2021). Application of DEA-based Malmquist productivity index on health care system efficiency of ASEAN countries. The International Journal of Health Planning and Management. 36(4): 1236-1250. https://doi.org/10.1002/hpm.3169.

  32. Tigga, N.S. and Sarkar, P. (2025). Has the efficiency and productivity of the health system in India improved during post-policy period? Application of the bootstrap DEA and Malmquist productivity index. Applied Economics. 57(11): 1239- 1256. https://doi.org/10.1080/00036846.2024.2311759.

  33. Timon, C.M., O’Connor, A., Bhargava, N., Gibney, E.R. and Feeney, E.L. (2020). Dairy consumption and metabolic health. Nutrients. 12(10): 3040.

  34. Vandana and Khetarpaul, N. (2017). Prevalence of nutritional deficiency signs among preschool children (1-3 years) of Hisar district of Haryana. Asian Journal of Dairy and Food Research. 36(1): 80-82. doi: 10.18805/ajdfr.v36i01.7463.

  35. Zadgaonkar, U. (2025). The impact of dairy-based nutrition and resistance training on muscle strength among geriatric adult. Asian Journal of Dairy and Food Research. 44: 107-114. doi: 10.18805/ajdfr.DR-2313.

  36. Zhang, X., Chen, X., Xu, Y., Yang, J., Du, L., Li, K. and Zhou, Y. (2021). Milk consumption and multiple health outcomes: Umbrella review of systematic reviews and meta-analyses in humans. Nutrition and Metabolism. 18(1): 7.
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    Full Research Article

    Government Health Expenditure and Sustainable Food Consumption: A State-Level Efficiency Analysis of Child Survival in India

    V
    Vivek Babbar1,2,*
    J
    Jitender Bhandari1
    1Economics, Christ (Deemed to be) University, Bangalore-560 029, Karnataka, India.
    2Zakir Husain Delhi College (Evening), University of Delhi, Delhi-110 002, India.

    ABSTRACT

    Background: The Central Government of India adopted the National Rural Health Mission (NRHM) in 2005, the Sustainable Development Goals (SDGs) in 2015, the National Health Policy (NHP) in 2017 and the POSHAN program for enhanced food nutrition in an attempt to increase Government Health Expenditure (GHE) and upgrade state-level health facilities.

    Methods: This paper analysis the efficiency of the GHE on child survival outcomes comparing National family health survey rounds 4 and 5 using Stochastic Frontier Analysis (SFA) and also to measure the state-wise efficiency of GHE on health outcomes.

    Result: The findings indicate that there is a steady increase in TE and a steady decrease in the technical inefficiency in all three outcomes in NFHS-5 compared to NFHS-4. Government health spending is found to be a statistically significant of child survival in both survey rounds and the elasticity of government health spending in NFHS-5 was higher. Conversely, the health centers per lakh population do not have a statistically significant independent effect. The negative relationship between female literacy, as a determinant of inefficiency, is statistically insignificant, which means that the current efficiency gains are the result of the enhanced performance of the health systems and not of the socio-demographic variations between states. The results highlight the effectiveness of efficiency-based health policies and the continued allocation of funds, such as increased state expenditure on nutritional foods and dairy products, to further enhance child survival rates in India.

    INTRODUCTION

    Government Health Expenditure (GHE) is an important ingredient of the government budget. According to the National Health Account Report, Fig 1 shows that India’s GHE increased significantly from 1.13% of GDP in 2014–15 to 1.84% in 2021-22, but is still short of the 2.5% GDP target. The government’s major attempts to achieve financial stability and Universal Health Coverage (UHC) for its residents are supported by the consistent rise in overall health spending. Even throughout the COVID-19 pandemic, the government prioritised primary care (55.9% of GHE), with large programs like Ayushman Bharat driving investment. Described as the largest government-funded health assurance program in the world, it aims to give over 10-12 crore impoverished and vulnerable families health coverage of ₹ 5 lakh per family annually for secondary and tertiary care hospitalisation. Studies by Deaton and Dreze (2009); Misra et al., (2011); George et al., (2024); Zadgaonkar (2025) and Kumar et al., (2025), further highlighted the significance of dietary intake of nutritious foods and dairy products in improving health outcomes in India. In response to combat malnutrition among children, expectant mothers, nursing mothers and teenage females, the Indian government launched the POSHAN Abhiyaan in 2018.

    Fig 1: The percentage of GDP (gross domestic product) allocated to government health spending (GHE).


           
    As a result, out-of-pocket (OOP) spending, which is a defining characteristic of private health expenditures in India, is declining to its proportion of Total Health Expenditure (THE) from over 62.6% in 2014-15 to roughly 47.1% in 2020-21 due to increased public spending. Significant interstate disparities are highlighted by National Family Health Survey (NFHS) data. For example, in Table 1 Kerala (KR) has an infant mortality rate (IMR) of about 4.4 per 1,000 live births, but states like Uttar Pradesh (UP) have an IMR of 50.4 per 1,000 live births. As presented in Table 1, these persistent regional disparities are a result of large and economically weaker states like Uttar Pradesh (UP), Bihar (BR), Madhya Pradesh (MP) and Chhattisgarh (CH) continuously falling behind better-performing southern states. In spite of these differences, NFHS-5 outperforms NFHS-4 in terms of living conditions, access to healthcare, child survival and fertility reduction. Improvements in results, however, may not always indicate effective resource use. When resources are used to produce the best possible health results, public spending is considered efficient. According to recent studies by Tigga and Sarkar (2025), inefficiencies in health expenditures rose from 17% in 2014-15 to 26% in 2019-20, indicating that higher spending is not enough on its own without improvements in resource allocation, governance and service delivery. Both nationally and internationally, a significant amount of research has been done on the efficiency and productivity of health systems. Studies on OECD countries (Afonso and Aubyn, 2011; Cetin and Bahçe, 2016; Samut and Cafrý, 2015), Sub-Saharan Africa (Grigoli and Kapsoli, 2018; Kinfu, 2013), the Eastern Mediterranean region (Masri and Asbu, 2018), ASEAN countries (Singh et al., 2021) and India (Acharya et al., 2019; Purohit, 2014; Mohanty and Bhanumurthy, 2020; Sarma and Kamble, 2017). Overall, the literature review assesses the effectiveness of GHE on health outcomes; however, no research has been conducted in India to examine the effectiveness of GHE with respect to health outcomes between NFHS 4 and NFHS 5 rounds. Between these two cycles, Significant policy changes, such as the adoption of the Ministry of Health and Family Welfare (2017), Ministry of Health and Family Welfare (2018)  and alignment with the Sustainable Development Goals (SDGs) in 2015. India had gains in important health indices and a rise in public health spending throughout this period, although regional inequities persisted. This situation offers a useful chance to determine whether higher spending has resulted in better efficiency. So the first objective is to compare the efficiency of GHE on health outcomes using two NFHS rounds and the second is to measure the state-wise efficiency of GHE on health outcomes.

    Table 1: States disparity in terms of health indicators (NFHS-5). Source: NFHS-5.


     
    Role of sustainable food consumption and dairy product on health outcome
     
    By lowering the risk of chronic, non-communicable diseases, sustainable food consumption which prioritizes nutrient-dense, locally sourced and ecologically friendly choices significantly improves health outcomes. As a “nutrient-dense powerhouse” that supplies vital nutrients (calcium, high-quality protein) required for health, dairy products play a complicated role in this. Dairy consumption, especially milk consumption, is typically linked to better development and health outcomes in children and adolescents, according to evidence from systematic reviews (Akyil et al., 2026 ; Zhang et al., 2021). The positive relationship between dairy consumption and metabolic health is further supported by narrative evidence (Timon et al., 2020). Increased consumption of fruits and vegetables is associated with increased life satisfaction and well-being, broader dietary patterns are also important (Holder, 2019). On the other hand, particularly in the least developed nations, rising food costs can exacerbate under nutrition and raise infant and child mortality (Lee et al., 2016; An, 2013). All things considered, sustainable diets greatly improve health outcomes.
           
    The rest of the study, Section 2, describes the study’s materials and methods, while Section 3 presents the result and discussion and Section 4 provides the conclusion and policy implications. 

    MATERIALS AND METHODS

    Twenty states were included in this study: Rajasthan (RJ), Assam (AS), Tamil Nadu (TN), BR, Gujarat (GJ), Haryana (HR), Himachal Pradesh (HP), Karnataka (KN), KR, MP, Maharashtra (MH), Odisha (OD), Punjab (PN) andhra Pradesh (AP), UP, JK, Jharkhand (JH), CH, Telangana (TN) and Uttarakhand (UK). These states, which together account for about 85.91% of India’s GDP, were chosen to guarantee both geographic representation and economic significance.
           
    In order to compare efficiency over time, the analysis is based on cross-sectional secondary data from two rounds of the NFHS-4, 2015-16 and NFHS-5, 2019-20. The study conceptualizes health systems as production units that transform inputs into desirable health outcomes. The primary input variable is Per Capita GHE(PCGHE) made by all government agencies, quasi-governmental organizations and donors in the event that funds are transferred through government organizations are all included in a State or UT’s GHE. The number of total health centers per lakh population (PCTHC), a control variable that represents health infrastructure, is also included in the study. After accounting for population size, this metric combines sub-centers (SCs), primary health centres (PHCs) and community health centres (CHCs), which are sourced from Rural Health Statistics, Statistics Division, Ministry of Health and Family Welfare. According to Filmer and Pritchett, 1999 and Bloom et al., (2004), government health spending by itself does not improve mortality indicators unless it is converted into actual service delivery by the entire health centre. The study also includes the inefficiency, female literacy variables, which affect the health outcome. Women’s illiteracy is less likely to Identify warning indicators of pregnancy and children’s illnesses and employ preventative measures, such as nutrition, cleanliness and immunization (Kinfu, 2013). Female literacy rate is sourced from the NFHS, MoHFW. Per capita values were calculated by the estimated population of the states for each year to obtain per capita values sourced from the National Commission on Population Projections.
           
    So the present study covers the mortality health indicators, IMR, NMR and Under-five Mortality Rate (U5MR). But we derived the survival rate from these health indicators because in SFA, the required outputs were “more is better.” The authors transform IMR, NNM and U5MR into infant survival rate (ISR), neo natal survival rate (NSR) and under five survival rate (U5SR), calculated as:
     
     
            
    These indicators reflect the number of surviving children per 1,000 live births and serve as appropriate output variables in the efficiency framework. Data from these indicators are sourced from the NFHS, MoHFW.
           
    The study uses Stochastic Frontier Analysis (SFA), a popular econometric method for assessing technological efficiency in manufacturing processes, to estimate efficiency (Aigner et al., 1977; Meeusen and van den Broeck, 1977; Coelli et al., 2005). The stochastic production frontier log-linear model equation used in this study is similar to the one proposed by Battese and Coelli (1995).
     
    ln qi = β0 + Σ βn ln Xni + Vi - Ui    ...(1)      
     
    The following efficiency equations are included in this article:
     
    lnISR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui           ...(2)
     
    lnNSR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui          ...(3)
     
    lnU5SR = β0 + Σ β1 ln PCGHE+ β2 PCTHC+ Vi-Ui          ...(4)
     
    Xni =   Input vector which is PCGHE and PCTHC that are associated with state i.
    qi = Health output of state i (ISR, NSR and U5SR).
    β = Vector of parameters to be estimated.
           
    There are two error term components in the model Ui and Vi, respectively, stand for technical inefficiency and the random component, which represents the impact of stochastic events outside the production unit’s control. For the two components of the error terms, four distinct distributional assumptions are made in the literature. This article used the half-normal distribution is the most widely assumed distribution and thus it is also the one employed here (Jondrow et al., 1982).
           
    Techincal Efficiency (TE) measures how well each production unit represented by a state in our analysis performs in proportion to its highest potential, given the resources now available to it. Theoretically, the TE score ranges from 0 (the least efficient) to 1 (the most efficient). The logarithmic form of the equation makes it easier to quantify elasticity and to interpret coefficient values, which represent percentage changes in one variable in relation to others.
           
    Following the Battese and Coelli (1995) one stage for inefficiency effects model, technical inefficiency is modeled as a function of state-level characteristics:
     
    Ui = Zi δ + Wi
     
    Zi = A vector of explanatory variables for inefficiency,
    δ = A vector of parameters.
    Wi = A random error term.
           
    In this article, the inefficiency equation includes the following variables:
    Ui = δ0 + δ2(lnFL)i + Wi
     
    lnFL= Logarithm of female literacy, which is the percentage of women 15 years of age or older who have never attended school. 

    RESULTS AND DISCUSSION

    Comparison between NFHS 4 and NFHS 5 Round
     
    Government health expenditures (GHE) had a positive and statistically significant impact on child survival outcomes in India, according to stochastic frontier analysis utilizing NFHS-4 and NFHS-5 data.  As shown in Table 2 and Fig 2, a 1% increase in per capita government health expenditure on a average elasticity for infant survival rate (ISR) rose from 0.738 (p<0.01) in NFHS-4 to 0.776 (p<0.05) in NFHS-5. Neonatal survival rate (NSR) elasticity increased from 0.671 (p<0.01) in NFHS-4 to 0.785 (p<0.05) in NFHS-5. The equivalent elasticity for the under-five survival rate (U5SR) rose from 0.715 (p<0.01) to 0.814 (p<0.05), keeping other variables constant. The larger elasticity estimates observed in NFHS-5 as compare to NFHS-4 it shows that improved nutrition and sustainable food consumption initiatives supported by public health programs may contribute to the beneficial effects of government health spending on child survival outcomes. On the other hand, in Table 2 shows that the number of health centers per lakh population is still statistically insignificant on any of the survival indicators in both rounds. The inclusion of female literacy as a driver of inefficiency is not statistically significant on all child survival rate, suggesting that its impact on efficiency increases is minimal. According to the Table 2, between NFHS-4 and NFHS-5, the mean TE for Infant Survival Rate (ISR) rose from 0.894 to 0.931, a 3.7 percentage point improvement. Similarly, the mean TE for Under-Five Survival Rate (U5SR) climbed from 0.905 to 0.933 and the mean TE for Neonatal Survival Rate (NSR) increased from 0.906 to 0.936. The higher technical efficiency scores observed in NFHS-5 of all indicators.

    Table 2: Comparative child health outcomes stochastic frontier results (NFHS-4 vs. NFHS-5).



    Fig 2: Differences between NFHS-4 and NFHS-5 in the elasticity coefficients of government health spending for infant survival rate (ISR), neonatal survival rate (NSR) and under-five survival rate (U5SR).


     
    Technical efficiency (TE) comparison by states between NFHS-4 and NFHS-5
     
    The state-level mean Technical Efficiency (TE) scores for Infant Survival Rate (ISR), Neonatal Survival Rate (NSR) and Under-Five Survival Rate (U5SR) in NFHS-4 and NFHS-5 are shown in Table 3. The mean TE increased from 0.894 to 0.931 for ISR, from 0.906 to 0.936 for NSR and from 0.905 to 0.933 for U5SR between the two survey rounds. As illustrated in Fig 3, Indian states achieved higher levels of technical efficiency during NFHS-5.While Kerala consistently recorded the highest efficiency scores across all indicators followed by Tamil Nadu, Maharashtra and Himachal Pradesh stayed close to the efficiency frontier, In contrast, Bihar recorded the lowest mean TE scores in NFHS-5, with values of 0.875, 0.881 and 0.879 for ISR, NSR and U5SR, followed by Rajasthan, Madhya Pradesh and Jharkhand continued to lag. Interestingly, Madhya Pradesh and Rajasthan showed the most gains in mean efficiency across child survival outcomes, suggesting better use of resources. Despite overall progress, persistent interstate disparities highlight variation in governance, healthcare delivery and health-system performance.

    Table 3: State-level scores of TE of infant, neonatal and under-5 survival outcomes of the NFHS-4 and NFHS-5 rounds.



    Fig 3: The top five and lowest five states in NFHS-5 according to average of mean technical efficiency (mean of ISR, NSR and U5SR).


           
    Government health spending is a positive and statistically significant predictor of child survival in both round which is similar to findings by earlier studies such as Mohanty and Bhanumurthy (2020); Tigga and Sarkar (2025). The result indicating that government spending can promote health through nutrition-support programs in addition to healthcare services. Initiatives like POSHAN Abhiyaan, Integrated Child Development Services (ICDS), Mid-Day Meal Scheme, Anaemia Mukt Bharat and supplemental nutrition programs encourage mothers and children in India to have access to wholesome, balanced diets. Food security and improved dietary quality are key elements of sustainable food consumption and are well known factors that affect children’s nutritional status and survival (Johnston et al., 2014; Vandana and Khetarpaul, 2017). Therefore, the indirect advantages of nutrition-sensitive interventions funded by public resources partially responsible for the observed improvement in child survival outcomes linked to increased government health. The result also shows higher efficiency observed in NFHS-5 its due to the better targeting, better service delivery and complementarities with socioeconomic characteristics could be the reasons for this increase.  Nevertheless, it appears that in both rounds the number of health centres per lakh population does not have a statistically significant influence, indicating that infrastructure expansion on its own is insufficient in the absence of improvements in service quality and utilization. The higher mean TE scores observed in NFHS-5 as compare to NFHS-4 suggest a more effective utilization of available health resources and a better conversion of government health expenditure into improved child survival outcomes and this indicates that the Indian states have been slowly approaching the boundary of best-practice production of health. There is a negative but negligible correlation between female literacy and inefficiency, suggesting that changes in the health system have contributed more to recent efficiency advances than sociodemographic factors. State-wise variation is also observed, Bihar, Madhya Pradesh and Jharkhand fall short of the efficiency frontier, while Kerala, Tamil Nadu, Maharashtra and Himachal Pradesh continuously perform close to it, similar efficiency variation observed in Afonso and Aubyn (2005), Dutu and Sicari (2016) and Mohanty and Bhanumurthy (2020) who found wide disparities in efficiency levels across countries and states. This disparity is due to structural and institutional limitations are shown by persistent interstate disparities.

    FUTURE RESEARCH DIRECTIONS

    Due to data limitations, future studies should directly measure dietary diversity, food security, nutritional intake and sustainable consumption pattern in order to assess their impact on child health outcomes and the effectiveness of the health system. In order to investigate the long-term association between government health spending and child health outcomes, the current analysis should be expanded by using panel data methodologies. Deeper understanding of interstate efficiency disparities may be obtained by incorporating indices of service delivery, healthcare accessibility and government quality. Localized inequities that are not represented at the state level may be further identified by district-level analysis.

    CONCLUSION

    Using Stochastic Frontier Analysis (SFA) based on NFHS-4 and NFHS-5 data, this study investigates the effectiveness of Government Health Expenditure (GHE) in enhancing child survival outcomes across Indian states. Across child, neonatal and under-five survival metrics, the results demonstrate decreased inefficiency and increased technical efficiency with time. GHE shows a statistically significant and favourable impact, with stronger effects in NFHS-5, suggesting improved efficacy through improved service quality and usage. Although female literacy exhibits the anticipated negative correlation with inefficiency, it is not statistically significant, indicating that sociodemographic differences have little impact. From a policy perspective, the results highlight the importance of sustained and targeted public investment in maternal and child health programs, particularly nutrition-focused initiatives such as POSHAN, as literature studies also show the impact of sustainable food consumption and dairy products on health outcomes. Furthermore, the decrease in inefficiency emphasizes how crucial better management, governance and efficient use of current resources are to attaining health benefits.

    ACKNOWLEDGEMENT

    The authors would like to sincerely thank all institutions and organizations that provided access to the secondary data used in this study. These databases were critical for conducting this analysis. This study received no specific funding.

    CONFLICT OF INTEREST

    No conflict of interest for this study.

    REFERENCES


    1. Acharya, D., Sahoo, B.K. and Venkatachalapathy, T.K. (2019). Health System and Health Expenditure Productivity Changes in Indian States: Has It Changed for the Better in the Post-Reform Period?. In Disease, Human Health and Regional Growth and Development in Asia. Singapore: Springer Singapore. (pp. 71-87).

    2. Afonso, A. and Aubyn, M.S. (2011). Assessing health efficiency across countries with a two-step and bootstrap analysis. Applied Economics Letters. 18(15): 1427-1430. https:// doi.org/10.1080/13504851.2010.54114.

    3. Afonso, A. and Aubyn, M.S. (2005). Non-parametric approaches to education and health expenditure efficiency in OECD countries. Journal of Applied Economics. 8(2): 227-246. https://doi.org/10.1080/15140326.2005.12040626.

    4. Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics. 6: 21-37.

    5. Akyil, S., Winkler, S., Meyer, D., Kiesswetter, E., Kussmann, M., Schwingshackl, L. and Hauner, H. (2026). Association between dairy intake and multiple health outcomes: A scoping review of systematic reviews and meta-analyses. European Journal of Clinical Nutrition. 80(1): 16-27.

    6. An, R. (2013). Effectiveness of subsidies in promoting healthy food purchases and consumption: A review of field experiments. Public Health Nutrition. 16(7): 1215-1228.

    7. Battese, G.E. and Coelli, T.J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics. 20(2): 325-332.

    8. Bloom, D.E., Canning, D. and Sevilla, J. (2004). The effect of health on economic growth: A production function approach. World Development. 32(1): 1-13.

    9. Cetin, V.R. and Bahce, S. (2016). Measuring the efficiency of health systems of OECD countries by data envelopment analysis. Applied Economics. 48(37): 3497-3507. https://doi.org/ 10.1080/00036846.2016.1139682.  

    10. Coelli, T.J., Prasada Rao, D.S., O’donnell, C.J. and Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis. Boston, MA: Springer US.

    11. Deaton, A. and Drèze, J. (2009). Food and nutrition in India: Facts and interpretations. Economic and Political Weekly. 44(7): 42-65. https://www.jstor.org/stable/40278509.

    12. Dutu, R. and Sicari, P. (2016). Public Spending Efficiency in the OECD: Benchmarking Health Care, Education and General Administration. OECD Economics Department Working Papers No. 1278. OECD Publishing. https://doi.org/ 10.1787/5jm3stxq46tg-en

    13. Filmer, D. and Pritchett, L. (1999). The impact of public spending on health: Does money matter?. Social Science and Medicine. 49(10): 1309-1323. 

    14. George, F., Rajeev, L.N., Bandyopadhyay, S., Baby, J., Sinha, S., Sachdev, H.S. and Thomas, T. (2024). The role of dairy consumption in the relationship between wealth and early life physical growth in India: Evidence from multiple national surveys. BMC Public Health. 24(1): 96.

    15. Grigoli, F. and Kapsoli, J. (2018). Waste not, want not: The efficiency of health expenditure in emerging and developing economies. Review of Development Economics. 22(1): 384-403.

    16. Holder, M.D. (2019). The contribution of food consumption to well- being. Annals of Nutrition and Metabolism. 74(Suppl. 2): 44-52.

    17. Johnston, J.L., Fanzo, J.C. and Cogill, B. (2014). Understanding sustainable diets: A descriptive analysis of the determinants and processes that influence diets and their impact on health, food security and environmental sustainability. Advances in Nutrition. 5(4): 418-429.

    18. Jondrow, J., Materov, I., Lovell, K. and Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics. 19: 233-238.

    19. Kinfu, Y. (2013). The efficiency of the health system in South Africa: Evidence from SFA. Applied Economics. 45(8): 1003- 1010. https: //doi.org/10.1080/00036846.2011.613787.

    20. Kumar, V., Akanksha, Singh, S., Yadav, A.K., Kumar, S. and Rathaur, A. (2025). The health benefits of ghee: A comparative analysis of Ayurvedic and modern scientific perspectives: A review. Asian Journal of Dairy and Food Research. 44(6): 890-898. doi: 10.18805/ajdfr.DR-2227.

    21. Lee, H.H., Lee, S.A., Lim, J.Y. and Park, C.Y. (2016). Effects of food price inflation on infant and child mortality in developing countries. The European Journal of Health Economics. 17(5): 535-551.

    22. Ministry of Health and Family Welfare. (2017). National Health Policy 2017. Government of India.

    23. ​Ministry of Health and Family Welfare. (2018). Ayushman Bharat: Comprehensive Primary Health Care through Health and Wellness Centres-Operational Guidelines. Government of India.

    24. Masri, M.D. and Asbu, E.Z. (2018). Productivity change of national health systems in the WHO Eastern Mediterranean Region: Application of DEA-based Malmquist productivity index. Global Health Research and Policy. 3(1): 22. https:// doi.org/10.1186/s41256-018- 0077-8.  

    25. Meeusen, W. and van der Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review. 18: 435-444.

    26. Misra, A., Singhal, N., Sivakumar, B., Bhagat, N., Jaiswal, A. and Khurana, L. (2011). Nutrition transition in India: Secular trends in dietary intake and their relationship to diet related non communicable diseases. Journal of Diabetes. 3(4): 278-292.

    27. Mohanty, R.K. and Bhanumurthy, N.R. (2021). Assessing public expenditure efficiency at the subnational level in India: Does governance matter?. Journal of Public Affairs. 21(2): e2173. https://doi.org/10.1016/j.asieco.2020.101224.

    28. Purohit, B.C. (2014). Efficiency of social sector expenditure in India: A case of health and education in selected Indian states. Healthcare in Low-Resource Settings. 2(1). https:// doi.org/10.4081/hls.2014.1866.

    29. Samut, P.K. and Cafrý, R. (2015). Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel Tobit. Social Indicators Research. 129(1): 113-132. https:// doi.org/10.1007/s11205-015-1094-3.  

    30. Sarma, J.V.M. and Kamble, P. (2017). Efficiency and Adequacy of Public Health System in Improving Health Outcomes: A SFA for Indian States. In Challenges and Issues in Indian Fiscal Federalism. Singapore: Springer Singapore. (pp. 143-158).

    31. Singh, S., Bala, M.M., Kumar, N. and Janor, H. (2021). Application of DEA-based Malmquist productivity index on health care system efficiency of ASEAN countries. The International Journal of Health Planning and Management. 36(4): 1236-1250. https://doi.org/10.1002/hpm.3169.

    32. Tigga, N.S. and Sarkar, P. (2025). Has the efficiency and productivity of the health system in India improved during post-policy period? Application of the bootstrap DEA and Malmquist productivity index. Applied Economics. 57(11): 1239- 1256. https://doi.org/10.1080/00036846.2024.2311759.

    33. Timon, C.M., O’Connor, A., Bhargava, N., Gibney, E.R. and Feeney, E.L. (2020). Dairy consumption and metabolic health. Nutrients. 12(10): 3040.

    34. Vandana and Khetarpaul, N. (2017). Prevalence of nutritional deficiency signs among preschool children (1-3 years) of Hisar district of Haryana. Asian Journal of Dairy and Food Research. 36(1): 80-82. doi: 10.18805/ajdfr.v36i01.7463.

    35. Zadgaonkar, U. (2025). The impact of dairy-based nutrition and resistance training on muscle strength among geriatric adult. Asian Journal of Dairy and Food Research. 44: 107-114. doi: 10.18805/ajdfr.DR-2313.

    36. Zhang, X., Chen, X., Xu, Y., Yang, J., Du, L., Li, K. and Zhou, Y. (2021). Milk consumption and multiple health outcomes: Umbrella review of systematic reviews and meta-analyses in humans. Nutrition and Metabolism. 18(1): 7.
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