Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis

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Previous studies have revealed the existing relationship of health spending with the reduction of malnutrition. Methods A quantitative approach is considered, with an explanatory type of research using panel data methodology considering the bidimensionality of the data, which allows quantifying this effect for the Peruvian case using the National Household Survey, data from the Central Reserve Bank of Peru, as well as information from the National Institute of Statistics and Informatics and the Transparency Portal of the Ministry of Economy and Finance in the period 2010-2020. Results The results show that public expenditure on health has a negative relationship with malnutrition; the rural sector has a positive relationship with malnutrition given the limitations present for access to adequate food. Similarly, the unemployment rate shows a positive relationship with malnutrition, given that being unemployed leads to a higher cause of malnutrition in the population, and the gross domestic product has a negative relationship with malnutrition, given that greater economic growth produces an impact on reducing malnutrition, with the greatest impact being on the rural population and the gross domestic product. Conclusions In the analysis period 2010-2020 in Peru, based on the panel data analysis, the impact of public health expenditure on reducing malnutrition is observed in 10 departments, achieving a reduction in malnutrition; while in 14 departments, this indicator has not been reduced. 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F1000Research 2025, 13 :990 ( https://doi.org/10.12688/f1000research.153477.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] Percy Junior Castro Mejía https://orcid.org/0000-0002-5345-5098 1 , Rogger Orlando Morán-Santamaría https://orcid.org/0000-0001-7037-097X 1 , Yefferson Llonto Caicedo https://orcid.org/0000-0003-0662-9064 2 , [...] Francisco Eduardo Cúneo Fernández https://orcid.org/0000-0002-7099-6793 1 , Nikolays Pedro Lizana Guevara https://orcid.org/0000-0002-2593-661X 1 , Milagros Judith Pérez Pérez https://orcid.org/0000-0003-1037-8392 1 , Lindon Vela Meléndez https://orcid.org/0000-0002-9644-7151 2 Percy Junior Castro Mejía https://orcid.org/0000-0002-5345-5098 1 , Rogger Orlando Morán-Santamaría https://orcid.org/0000-0001-7037-097X 1 , [...] Yefferson Llonto Caicedo https://orcid.org/0000-0003-0662-9064 2 , Francisco Eduardo Cúneo Fernández https://orcid.org/0000-0002-7099-6793 1 , Nikolays Pedro Lizana Guevara https://orcid.org/0000-0002-2593-661X 1 , Milagros Judith Pérez Pérez https://orcid.org/0000-0003-1037-8392 1 , Lindon Vela Meléndez https://orcid.org/0000-0002-9644-7151 2 PUBLISHED 17 Jan 2025 Author details Author details 1 Universidad Cesar Vallejo, Avenida Víctor Larco, Trujillo, La Libertad, 1700, Peru 2 Universidad Nacional Pedro Ruiz Gallo, Juan XXIII 391, Lambayeque, 14013, Peru Percy Junior Castro Mejía Roles: Methodology, Writing – Review & Editing Rogger Orlando Morán-Santamaría Roles: Investigation, Methodology, Writing – Original Draft Preparation Yefferson Llonto Caicedo Roles: Investigation, Methodology, Writing – Original Draft Preparation Francisco Eduardo Cúneo Fernández Roles: Validation, Writing – Original Draft Preparation Nikolays Pedro Lizana Guevara Roles: Software, Visualization Milagros Judith Pérez Pérez Roles: Supervision, Visualization Lindon Vela Meléndez Roles: Methodology, Supervision, Visualization OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background The study analyzes the impact of public health spending on malnutrition among Peruvians, using data from the National Household Survey, the Central Reserve Bank of Peru, the National Institute of Statistics and Informatics and the Ministry of Economy and Finance from 2010. -2020. Previous studies have revealed the existing relationship of health spending with the reduction of malnutrition. Methods A quantitative approach is considered, with an explanatory type of research using panel data methodology considering the bidimensionality of the data, which allows quantifying this effect for the Peruvian case using the National Household Survey, data from the Central Reserve Bank of Peru, as well as information from the National Institute of Statistics and Informatics and the Transparency Portal of the Ministry of Economy and Finance in the period 2010-2020. Results The results show that public expenditure on health has a negative relationship with malnutrition; the rural sector has a positive relationship with malnutrition given the limitations present for access to adequate food. Similarly, the unemployment rate shows a positive relationship with malnutrition, given that being unemployed leads to a higher cause of malnutrition in the population, and the gross domestic product has a negative relationship with malnutrition, given that greater economic growth produces an impact on reducing malnutrition, with the greatest impact being on the rural population and the gross domestic product. Conclusions In the analysis period 2010-2020 in Peru, based on the panel data analysis, the impact of public health expenditure on reducing malnutrition is observed in 10 departments, achieving a reduction in malnutrition; while in 14 departments, this indicator has not been reduced. READ ALL READ LESS Keywords public health expenditure, malnutrition, economy, panel data. Corresponding Author(s) Rogger Orlando Morán-Santamaría ( [email protected] ) Close Corresponding author: Rogger Orlando Morán-Santamaría Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Castro Mejía PJ et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Castro Mejía PJ, Morán-Santamaría RO, Llonto Caicedo Y et al. Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.12688/f1000research.153477.3 ) First published: 02 Sep 2024, 13 :990 ( https://doi.org/10.12688/f1000research.153477.1 ) Latest published: 17 Jan 2025, 13 :990 ( https://doi.org/10.12688/f1000research.153477.3 ) Revised Amendments from Version 2 We have made the following improvements: - In Introduction: The introduction has been restructured with wording up to four paragraphs, the amount recommended by the peer reviewer. - Methodology: The wording of the methodology has been complemented, with the purpose of understanding the results found. Also, the “ Procedure” item was eliminated , since it was redundant and was added in the methodology items. We have made the following improvements: - In Introduction: The introduction has been restructured with wording up to four paragraphs, the amount recommended by the peer reviewer. - Methodology: The wording of the methodology has been complemented, with the purpose of understanding the results found. Also, the “ Procedure” item was eliminated , since it was redundant and was added in the methodology items. See the authors' detailed response to the review by Paraskevi Seferidi READ REVIEWER RESPONSES Introduction Hunger and malnutrition remain persistent issues that severely affect the most vulnerable populations worldwide. According to the Food and Agriculture Organization of the United Nations United Nations Food and Agriculture Organization (2021) , more than 657 million people faced undernourishment in 2020, a figure exacerbated by the COVID-19 pandemic and indicative of the complexity of achieving the Sustainable Development Goal (SDG) of eradicating hunger and malnutrition by 2030 ( Ritchie et al., 2018 ; Madsen et al., 2022 ; Mottaleb et al., 2021 ; Sridhar et al., 2023 ; Katoch, 2024 ). In Latin America and the Caribbean, 14 million people suffer from food insecurity, a problem intertwined with social and economic inequality ( Casado et al., 2021 ; Cambra-Fierro et al., 2022 ; Liu et al., 2023 ; Vollset et al., 2024 ). In this context, public health spending has been identified as a crucial determinant in improving health and nutrition indicators. However, significant disparities in resource allocation persist at the regional and national levels. In Peru, public health expenditure barely exceeds 3% of GDP, ranking below other countries in the region ( Haldane et al., 2022 ; Pan American Health Organization, 2018 ; Vázquez-Rowe & Gandolfi, 2020 ). This level of investment, insufficient to address the country’s structural challenges, contributes to maintaining high malnutrition rates, particularly in rural communities where access to food and health services is more limited ( Velásquez et al., 2021 ). Despite the abundance of studies exploring associations between public health spending and various health indicators, a critical gap remains in the literature regarding the specific relationship between health spending and malnutrition in the Peruvian context. Previous studies, such as those by Bernet et al. (2018) and Calva & Ruiz (2021) , have pointed out that the impacts of health spending can vary depending on contextual factors such as the level of rurality and socioeconomic dynamics. However, few studies have comprehensively addressed this relationship in Peru, taking into account regional heterogeneity and the complexity of variables involved, such as unemployment, inflation, and economic growth. This gap underscores the need for an in-depth analysis of how public health spending can contribute to reducing malnutrition in a country marked by structural inequalities ( Caballero & Sánchez, 2021 ; Leung & Wolfson, 2019 ; Mogues & Billings, 2019 ). The objective of this study is to analyze the impact of public health spending on malnutrition in Peru during the period 2010–2020, using a panel data model that allows for the evaluation of regional differences and associated factors. This approach seeks not only to contribute to the scientific understanding of the phenomenon but also to generate empirical evidence that informs the design of more effective public policies to address malnutrition in highly vulnerable contexts. Methods This study employs a quantitative approach with an explanatory design, based on a panel data model that analyzes the relationship between public health spending and malnutrition in Peru during the 2010–2020 period. This method integrates longitudinal and cross-sectional information, providing a robust analysis of the temporal and regional dynamics of the variables studied ( Bernet et al., 2018 ; Calva & Ruiz, 2021 ; Edney et al., 2018 ; Antelo et al., 2017 ; Guo et al., 2019 ; Fallah et al., 2021 ; Huaripuma, 2022 ; Vera, 2019 ; Guzmán, 2021 ). Methodological design The methodological approach considers the traditional quantitative approach and correlational type of research, given that it analyzes the impact of public health spending on malnutrition among Peruvians, using data from the National Household Survey, the Central Reserve Bank of Peru, the National Institute of Statistics and Informatics and the Ministry of Economy and Finance from 2010-2020 ( National Institute of Statistics and Informatics, 2020 ). Sample The sample size consists of 24 departments of Peru during the period 2010-2020, considering that for quantitative research according to Hernández & Mendoza (2018) the minimum sample size for causal or comparative analysis is 64 data, which implies that the sample is non-probabilistic, given the careful and controlled choice of data that have fed the variables previously in the problem statement. The sampling method is non-probabilistic as it addresses the entirety of the data, totaling 2,112 observations, involving departmental analysis for the period 2010-2020. Document analysis was used as the technique for measuring the variables, and the observation guide was employed as the instrument. This tool is used to systematically and structurally collect data during an observation or study. Data were obtained from the microdata database of the National Household Survey for the years 2010 to 2020. The data were extracted from module 01 and 02 (Household Member Characteristics), module 03 (Education), module 04 (Health), module 05 (Employment and Income), module 07 (Food and Beverage Expenditures), and module 08 (Charitable Institutions) published on the website of the National Institute of Statistics and Informatics. This annual survey allows for the tracking of indicators on living conditions and is conducted nationwide across the 24 departments of the country. Additionally, data on the percentage variation of the price index by department for the period 2010-2020 and the gross value added by department at constant prices from 2007, compiled by the National Institute of Statistics and Informatics, were used and published in its economic statistics section. For the collection of public health expenditure data, departmental data reflecting the amount spent in millions of soles were obtained from the Economic Transparency Portal of the Ministry of Economy and Finance for the period 2010-2020. Finally, the database of the value of all goods and other market services exported to the rest of the world by department, measured in FOB value in millions of dollars, from the Central Reserve Bank of Peru for the period 2010-2020 was used. This data reflects the registration of sales abroad of goods and services by a resident company in Peru, including both traditional and non-traditional products. Data analysis method The estimation of malnutrition is based on the estimation of the caloric deficit and the basal metabolic rate using the methodology by Herrera (2002) which defines the caloric norm for Peru as a total of 2,318 calories per capita per day. This is based on the data from the National Institute of Statistics and Informatics, considering the average weight estimates by sex and age, classified for children under 10 years and those over 10 years by FAO ( Swindale & Ohri, 2004 ). For calculating the basal metabolic rate (BMR) for those over 10 years old, corrections were made based on estimates for Latin American countries (Bengoa et al., as cited in Herrera, 2002 ). Two options were considered: moderate activity for those over 10 years old in urban areas and intense activity for those over 10 years old in rural areas. Caloric deficit is used in this study as an indicator of household nutritional status, rather than employing the broader term "malnutrition." This approach aligns with the official practices of INEI for measuring monetary poverty and food needs in the country. • Minimum caloric reference: According to INEI and the methodology proposed by Herrera (2002), the minimum caloric requirement for the Peruvian population is set at 2,318 kilocalories (kcal) per person per day. This value represents the threshold necessary to meet the basic energy needs of an average individual in the Peruvian context. • Calculation of caloric intake: Based on data from ENAHO, per capita caloric intake in each household is calculated, considering the quantity and type of foods consumed and using food composition tables provided by INEI. • Determination of caloric deficit: A household is defined as having a caloric deficit if its per capita caloric intake is below 2,318 kcal per day. The percentage of households with a caloric deficit is then used as the dependent variable in the analysis. From the data obtained, which include time series and cross-sectional data, which allows the analysis of multiple observations over time for the same sample unit relevant to each department of Peru analyzed in the period 2010-2020, the econometric model called panel data is used, since it allows obtaining the largest amount of information and at the same time controlling the unobservable variability that is constant over time but may differ between subjects, reducing bias in the results ( Wooldridge, 2010 ). The panel data model helps to analyze changes over time and to identify causal relationships between variables that may vary between periods and reduce collinearity for better accuracy of estimates, allowing to increase the efficiency and power of estimates, by obtaining more robust and reliable results ( Wooldridge, 2010 ). On the other hand, the Generalized Least Squares (GLS) model is used since it becomes a fundamental tool when working with structural equations that present problems of heteroscedasticity and autocorrelation ( Wooldridge, 2010 ). By applying the GLS model, it is possible to obtain regression coefficients that more accurately reflect the relationship between variables, even when the data present variability and dependence in the errors. This is especially important in structural equations, where the complexity of the relationships between variables can make problems of heteroscedasticity and autocorrelation more common and, therefore, the use of GLS is essential to guarantee the validity and precision of the results obtained ( Wooldridge, 2010 ). The panel data model, based on Mushkin’s theory, will be used for the estimation, showing the relationship between malnutrition behavior explained by public health expenditure ( Calva & Ruiz, 2021 ). The model utilizes two equations. Through this model, the impact of public health expenditure on the malnutrition of Peruvians is quantified. Malnutrition, measured by caloric deficit, is the dependent variable, while public health expenditure is the independent variable. Control variables include inflation, rural population, unemployment, education, exports, and gross domestic product. To ensure consistent estimations and an adequate analysis, variables such as public health expenditure, rural population, education, exports, and gross domestic product have been transformed into logarithms. Additionally, to estimate the regression with control variables, the structural equations of the variables under analysis are used ( Wooldridge, 2010 ). Equation 1 establishes the relationship between the dependent variable malnutrition and public health expenditure by department 𝑖=1,2, …, 24i=1,2, …, 24 in the year t=2010−2020 t=2010−2020. (1) Desnt i , t = Bo + B 1 ( gsalud i , t ) + e i , t On the other hand, the incorporation of control variables for the robustness of the model includes variables such as inflation, rural population, unemployment, education, exports, and gross domestic product, which are reflected in the following equation: (2) Desnt i , t = Bo + B 1 ( gsalud i , t ) + B 2 ( infl i , t ) + B 3 ( prural i , t ) + B 4 ( desem i , t ) + B 5 ( educ i , t ) + B 6 ( export i , t ) + B 7 ( pbi i , t ) + e i , t In Equation 2 , structural equations will be used to evaluate the interdependencies, expressing the mentioned interrelationship in four equations: (3) Gsalud i , t = Bo + B 1 ( Desnt i , t ) + B 4 ( desem i , t ) + e i , t (4) Prural i , t = Bo + B 1 ( educ i , t ) + e i , t . (5) Desem i , t = Bo + B 1 ( Desnt i , t ) + B 2 ( infl i , t ) + B 5 ( educ i , t ) + e i , t (6) Pbi i , t = Bo + B 1 ( export i , t ) + B 2 ( infl i , t ) + B 3 ( desem i , t ) + e i , t Given the presence of heteroscedasticity and autocorrelation in the structural equations, confirmed by Breusch and Pagan (1979) tests for heteroscedasticity and Wooldridge (2010) tests for autocorrelation, a Generalised Least Squares (GLS) model was employed. The GLS estimator accounts for these violations of classical linear regression assumptions, providing more efficient and unbiased estimates. Additionally, a Hausman (1978) specification test was conducted to determine the appropriateness of fixed or random effects. For the estimation of the relevant control variables for the study according to the objective of the research that seeks to quantify the impact of public health spending on malnutrition of Peruvians during the years 2010-2020, the theoretical review and availability of the data described above have been considered, taking as a basis the theory of ( Mushkin, 1962 ) where it explains that the behavior of malnutrition is explained by public spending on health and the control variables considered by ( Calva & Ruiz, 2021 ) that includes inflation, rural population, unemployment, education, exports and gross domestic product in order to reduce the bias of the estimates; applying said model with the data of Peru in the period 2010-2020. Panel data models are based on several assumptions that allow estimates to be consistent, efficient and unbiased, as detailed below ( Beltrán & Castro, 2010 ). • No perfect multicollinearity: It is assumed that there is no exact linear relationship between the independent variables. In other words, no explanatory variable should be an exact linear combination of the others. • Homoscedasticity and No autocorrelation of errors: In the case of random effects models, it is assumed that the errors have a constant variance and are not correlated over time, considering that Generalized Least Squares was used to correct the problem of heteroscedasticity and autocorrelation. • No correlation between individual effects and independent variables: In random effects models, it is assumed that individual effects (the variables) are not correlated with the explanatory variables of the model; for our research, fixed effects were used. • Strict exogeneity: This assumption implies that the errors are not correlated with the independent variables of the model in all time periods for each individual. • No serial correlation: For fixed effects models, it is assumed that the errors are not correlated over time. The variables used for the model estimation are detailed in Table 1 . Table 1. Variables used in the model. Variable Description Source Malnutrition (desn i,t) The population by department located below the minimum level of caloric requirements, expressed as the percentage of the population by region whose intake of safe and nutritious food is insufficient to meet their minimum energy needs National Household Survey 2010-2020 Public Health Expenditure (gsalud i,t) Level of health expenditure, expressed by the amount accrued by regional governments in the health function, in thousands of soles Economic Transparency Portal, Ministry of Economy and Finance 2010-2020 Rural Population (prural i,t) Population by department in rural areas, calculated as the difference between the total population and the urban population National Household Survey 2010-2020 Unemployment (desem i,t) Percentage of the active population by region that is unemployed but seeking work and available for work National Household Survey 2010-2020 Inflation (inf i,t) Percentage variation of the price index by department National Institute of Statistics and Informatics Education (educ i,t) Average years of education by department National Household Survey 2010-2020 Exports (export i,t) Value of all goods and other market services exported to the rest of the world by department, in thousands of soles Central Reserve Bank of Peru 2010-2020 Gross Domestic Product (y i,t) Gross Value Added by department, at constant 2007 prices, in thousands of soles National Institute of Statistics and Informatics Data processing For the estimation of the undernutrition variable, the National Household Survey database for the period 2010-2020 was considered. Eviews 12 is used for the estimation of the panel data model, considering the control variables that are subjected to the proposed equations to analyze the interrelationship between the variables. This allows for both descriptive statistical treatment and the estimation of the proposed panel data model and the Generalized Least Squares (GLS) model. An academic license for the use of Eviews 12 software is available, registered under the name Lindon Vela Meléndez. The license details are as follows: Serial number: Q1208886 - D49010AF - 9D854485. The software can be downloaded from the following link: http://www.eviews.com/download/student12 . Autocorrelation detection was performed using the Lagrange multiplier tests of Breusch & Pagan (1979) and Wooldridge (2010) . In addition, the Hausman (1978) test was used to choose between a fixed or random effects model. We assessed the presence of autocorrelation in the model residuals using two statistical tests. First, the Breusch and Pagan (1979) Lagrange multiplier test was applied, which examines the null hypothesis of no autocorrelation against the alternative of autocorrelation up to a specified order. Second, the Wooldridge (2010) test, designed specifically to detect first-order autocorrelation in panel data, was used. To determine the appropriate specification of the model, we perform a Hausman (1978) specification test. This test compares fixed and random effects estimators under the null hypothesis that individual specific effects are uncorrelated with the explanatory variables. Ethical considerations Data for this study were obtained from the National Household Survey (ENAHO) conducted by the National Institute of Statistics and Informatics (INEI). The ENAHO, as a publicly available and freely accessible dataset on the INEI website, adheres to rigorous ethical standards to protect the confidentiality of participants. Furthermore, ENAHO data by INEI are anonymised before being made publicly available. This ensures that individual responses cannot be linked to specific participants. Although we cannot provide specific details about the consent process for each individual participant in the ENAHO survey, INEI, as a reputable government institution, is committed to ethical research practices and to obtaining informed consent. In the ethical use of public data obtained by the INEI in the ENAHO, we adhere to the principles of intellectual honesty, truthfulness, transparency, human integrity, respect for intellectual property, justice and responsibility, the study aims to comply with the “Code of Ethics in Research of the Universidad César Vallejo, version 01; by University Council Resolution N° 0340-2021-UCV”. We have used the data for research purposes only, as permitted by INEI’s terms of use. We have not attempted to re-identify any individual, and our analysis does not include any personally identifiable information. By using publicly available anonymised data and adhering to ethical principles in our research, we aim to minimise any potential ethical concerns while leveraging valuable information for the benefit of public health research. Results The results of the estimated regressions, considering the Wooldridge test (2010), indicate the presence of autocorrelation in the various panels. Additionally, the Breusch and Pagan (1979) Lagrange Multiplier test shows that the estimates presented heteroscedasticity issues. These issues have been corrected using Generalized Least Squares (GLS) to address the problems of heteroscedasticity and autocorrelation. Regression of malnutrition with control variables Table 2 shows the results of the panel data estimation, where it is observed that public health expenditure, rural population, unemployment, and gross domestic product are statistically significant at the 5% level, considering Equation 2 . Table 2. Regression of malnutrition with control variables. Explanatory variable Levels Fixed effects Random effects Constant 3.0357 107.5500 48.1448 * (0.8059) * (0.0000) * (0.0432) Log_salud -2.7035 -2.5631 -2.5375 * (0.0000) * (0.0000) * (0.0000) Log_rural 11.4642 4.5941 5.3864 * (0.0000) * (0.017) * (0.0015) Desem 6.2010 2.2766 2.3181 * (0.0000) * (0.0000) * (0.0007) Log_y -1.2916 -4.2847 -0.9685 * (0.0317) * (0.036) * (0.4510) R-squared 0.332521 0.856703 0.390645 Test Hausman 0.0000 0.0000 Test Breusch-Pagan 0.0000 0.0000 0.0000 Durbin Watson stat 0.623864 1.28741 1.15187 Fixed effects Yes Yes Dynamic Effects No No Observations 264 264 264 * Significance p<0.05. It is observed that for each 1% increase in public health expenditure, malnutrition decreases by 2.6%. Conversely, a 1% increase in the rural population leads to a 4.6% increase in malnutrition. Additionally, a 1% increase in the unemployment rate results in a 2.3% increase in malnutrition, while a 1% increase in gross domestic product leads to a 4.3% decrease in malnutrition. The variables with the most significant impact during the analysis period 2010-2020 are the rural population and the gross domestic product. Regression of public health expenditure with malnutrition and unemployment Table 3 shows the analysis of public health expenditure considering structural Equation 3 . The results indicate that chronic malnutrition and unemployment are significant at the 5% confidence level. It explains that malnutrition has a negative effect on public health expenditure due to the limited importance given to public malnutrition policies, with public budgets being prioritized for other sectors. Table 3. Regression of public health expenditure with malnutrition and unemployment. Explanatory variable Levels Fixed effects Random effects Constant 20.3901 22.7669 20.7418 * (0.0000) * (0.0000) * (0.0000) Desn -0.0602 -0.1598 -0.0751 * (0.0000) * (0.0000) * (0.0000) Desem 0.1136 0.3977 0.1575 * (0.3142) * (0.0114) * (0.2568) R-squared 0.1395 0.447182 0.178649 Test Hausman 0.0000 0.0000 Test Breusch-Pagan 0.0000 0.0000 0.0000 Durbin Watson stat 1.842586 1.80502 1.82413 Fixed effects Yes Yes Dynamic effects No No Observations 264 264 264 * Significance p<0.05. It was observed that for each 1% increase in the malnutrition rate, public health expenditure decreases by 0.16%. Meanwhile, a 1% increase in the unemployment rate leads to a 0.40% increase in malnutrition. Regression of rural population with education Table 4 shows the analysis of the rural population considering structural Equation 4 . The results indicate that education is significant at the 5% confidence level, explaining that education has a negative effect on the rural population. This is mainly due to the trend that as the human capital of the population increases, there is a migration from rural to urban areas, moving to central zones to improve their income levels. Table 4. Regression of rural population with education. Explanatory variable Levels Fixed effects Ramdom effects Constant 13.0294 10.3153 10.9851 * (0.0000) * (0.0000) * (0.0000) Log_educ -2.8455 -1.4636 -1.8046 * (0.0000) * (0.0000) * (0.0000) R-squared 0.569582 0.952405 0.129147 Test Hausman 0.0000 0.0000 Test Breusch-Pagan 0.0000 0.0000 0.0000 Durbin Watson stat 0.214215 1.31476 1.09918 Fixed effects Yes Yes Dynamic effects No No Observations 264 264 264 * Significance p<0.05. It was observed that for each 1% increase in the average years of education, the malnutrition rate decreases by 1.46%. Regression of unemployment with education, malnutrition, and inflation Table 5 shows the analysis of unemployment considering structural Equation 5 . The results indicate that chronic malnutrition, inflation, and education are significant at the 5% confidence level. It explains that malnutrition has a positive effect on unemployment, as do education and inflation. This shows that high levels of malnutrition lead to high unemployment rates, indicating that nutritional imbalance causes problems in performing physical or intellectual labor, affecting worker productivity. Table 5. Regression of unemployment with education, malnutrition, and inflation. Explanatory variable Levels Fixed effects Random effects Constant -6.975429 -1.781961 -5.6962 * (0.0000) * (0.0188) * (0.0000) Log_educ 4.306179 1.800031 3.7822 * (0.0000) * (0.0148) * (0.0000) Desn 0.019167 0.010627 0.0108 * (0.0000) * (0.0025) * (0.0179) Inf 0.014043 0.009445 0.0141 * (0.0434) * (0.0390) * (0.0200) R-squared 0.470294 0.696024 0.205113 Test Hausman 0.0000 0.027500 Test Breusch-Pagan 0.0000 0.0000 0.0000 Durbin Watson stat 0.983788 0.86534 1.245992 Fixed effects Si Si Dynamic effects No No Observations 264 264 264 * Significance p<0.05. Additionally, the positive relationship between education and unemployment is explained by the excess accumulation of human capital in Peru, which exceeds the available job opportunities due to limited positions. Regarding inflation, the positive relationship with unemployment contradicts the Phillips Curve theory (1958) which suggests an inverse relationship between inflation and unemployment. According to Campoverde et al. (2016) this positive relationship depends on the economic context of each country. In Peru, the loss of purchasing power and delayed investment due to price fluctuations result in a decline in business confidence, creating an uncertain climate that impacts higher unemployment rates. It was observed that for each 1% increase in the malnutrition rate, unemployment increases by 0.01%. Additionally, a 1% increase in the average years of education leads to a 1.80% increase in unemployment, and a 1% increase in inflation causes unemployment to rise by 0.01%. Regression of gross domestic product with exports, unemployment, and inflation Table 6 shows the analysis of gross domestic product (GDP) considering structural Equation 6 . The results indicate that exports, unemployment, and inflation are significant at the 5% confidence level. The findings explain that exports have had a positive effect on GDP during the analyzed period, as higher exports result in a positive balance of payments, impacting foreign exchange and terms of trade, thereby fostering GDP growth. Conversely, unemployment shows a negative relationship with GDP due to the still high unemployment rates in Peru, which negatively affect economic growth by reducing private investment and capital attraction. Inflation also shows a negative relationship with GDP, as the inflationary spiral leads to a loss of purchasing power, consequently affecting economic growth. This is consistent with Fernando (2016) , who evidenced the negative relationship between inflation and economic growth for Honduras, and Uribe (2006), who noted that demand shocks negatively affect GDP and prices in Bolivia, while supply shocks have had a positive long-term impact on GDP and a decline in price levels. Table 6. Regression of gross domestic product. Explanatory variable Levels Fixed effects Random effects Constant 10.0677 14.7209 13.7375 *(0.0000) *(0.0000) *(0.0000) Log_export 0.4132 0.0957 0.1659 *(0.0000) *(0.0000) *(0.0000) Desem 0.0491 -0.0435 -0.0468 *(0.2850) *(0.0000) *(0.0132) Inf -0.0065 -0.0032 -0.0024 *(0.3115) *(0.0000) *(0.0939) R-squared 0.627458 0.98757 0.319472 Test Hausman 0.0000 0.0000 Test Breusch-Pagan 0.0000 0.0000 0.0000 Durbin Watson stat 0.0874 0.52895 0.39419 Fixed effects Si Si Dynamic effects No No Observations 264 264 264 ** Significance p<0.05. It was detailed that for each 1% increase in the value of exports, GDP increases by 9.5%. Meanwhile, for each 1% increase in the unemployment rate, GDP decreases by 4.3%, and a 1% increase in inflation would cause GDP to decrease by 0.32%. Effect of public health expenditure on malnutrition by department In the analysis of the impact of public health expenditure on the reduction of malnutrition by department shown in Figure 1 , it is observed that the greatest impact is in the Ucayali department, which reduced the malnutrition rate by 19.17%, followed by Madre de Dios with a reduction of 12.62%, and Ica with a reduction of 12.97%. Conversely, in Pasco, public health expenditure led to an increase in malnutrition by 17.61%, in Arequipa by 15.18%, in Lima by 9.85%, and in Cajamarca by 8.53%. This variability in the impact of public health expenditure on malnutrition during the period 2010-2020 indicates differing efficiencies and effectiveness of public health spending across departments. In 14 departments, including Pasco, Arequipa, Cajamarca, La Libertad, and Lima, public health expenditure did not achieve a reduction in the malnutrition indicator, with the population not meeting the minimum required caloric intake during the period 2010-2020. Figure 1. Impact of Public Health Expenditure on Malnutrition by Department. Note. The abbreviations of the regions of Peru are AM (Amazonas), AN (Ancash), AP (Apurímac), AR (Arequipa), AY (Ayacucho), CA (Cajamarca), CU (Cusco), HU (Huánuco), HV (Huancavelica), IC (Ica), JU (Junín), LA (Lambayeque), LL (La Libertad), LI (Lima), LO (Loreto), MD (Madre de Dios), MO (Moquegua), PA (Pasco), PI (Piura), PU (Puno), SM (San Martín), TA (Tacna), TU (Tumbes), and UC (Ucayali). Figure 2 shows the interrelationship of the variables in the proposed structural equations, where it has been evidenced that public health expenditure has not had the expected impact, as malnutrition levels have increased in Peru. The rural sector shows the most significant relevance with malnutrition due to limitations in accessing adequate food because of low incomes. Thus, the unemployment rate has a positive relationship with malnutrition, demonstrating that the unemployed population would have lower adequate calorie consumption, highlighting a positive association with malnutrition. Meanwhile, the sustained growth of gross domestic product (GDP) produces a negative effect in reducing malnutrition, with the most significant impact being related to the rural population and GDP. Figure 2. Dynamics of the identified structural equations in the departments of Peru. Note. Result of the interactions of regressions in the identified structural equations in the departments of Peru Additionally, it is observed that exports have positively influenced GDP, being considered a pillar of the Peruvian economy’s growth through the diversification of the productive matrix, which leads to a negative relationship with unemployment. Inflation, on the other hand, shows a negative relationship with GDP, as the inflationary spiral causes a loss of purchasing power, thereby affecting economic growth. Discussion The analysis of the impact of public health expenditure on the malnutrition of Peruvians during the years 2010-2020 shows a negative effect of public health expenditure on malnutrition. These results align with Huaripuma (2022) , Vera (2019) , and Guzmán (2021) , who detailed in their research in Peru the indirect relationship between public health expenditure and the reduction of the chronic malnutrition percentage in children under five years old. It is essential to highlight the interrelation with other variables observed in the proposed structural equations. Literature has evidenced that exports have had a positive effect on GDP during the analyzed period. Higher exports result in a positive balance of payments, impacting foreign exchange and terms of trade, thereby fostering GDP growth. In contrast, unemployment shows a negative relationship with GDP due to the still high unemployment rates in Peru, which negatively affect economic growth by reducing private investment and capital attraction. Inflation also shows a negative relationship with GDP, as the inflationary spiral leads to a loss of purchasing power, consequently affecting economic growth. This is supported by Bhuyan et al. (2020) who found that despite high economic growth in India, the country still faces the global problem of nutritional deficit, with the poor and very poor facing food insecurity alongside the middle class, causing issues among disadvantaged groups. However, Bernet et al. (2018) consider that the effectiveness of public health expenditure shows the challenges of aggregated public health spending, the problem of endogeneity, and the serial correlation between expenditures and outcomes, finding an inverse association between public health expenditure and the infant mortality rate. Similarly, Biadgilign et al. (2019) evidence that child malnutrition remains a significant problem in low- and middle-income countries, with adequate governance, urbanization, and public health expenditure having effects on child malnutrition. Peru is considered an upper-middle-income country that has been implementing public policies to reduce poverty through various social programs. According to Brar et al. (2020) a notable reduction in malnutrition has been observed in the Central and Western regions, involving multiple factors such as socioeconomic indicators, reductions in inequalities, and greater access to health services. The latter is a key element in reducing health, water, and sanitation gaps, resulting from better economic conditions due to Peru’s economic growth exceeding 5% of GDP. This regional analysis has allowed for greater public resources via public investment, reducing basic infrastructure gaps and contributing to the fight against malnutrition, corroborating the importance of public expenditure in various basic sectors. However, Peru faces a double burden of malnutrition, as stated by Quinteros et al. (2024) where not only prioritizing policies on malnutrition due to historically high levels is necessary, but also considering impacts related to intrafamily distribution and food quality used in regional programs like Qaliwarma. Another state program in Peru’s fight against poverty is Juntos, but in terms of cost-effectiveness, it still faces regional challenges, as indicated by Brar et al. (2020) . Addressing the underlying causes of household targeting for active policy implementation requires leadership and effectiveness from policymakers and program leaders against malnutrition in the context of a politically uncertain agenda in Peru. This leads to an increased gap between the poor and the rich, as well as between rural and urban areas, and highlights the need for basic health and sanitation infrastructure to support a multidimensional approach beyond just conditional cash transfers. Thus, the institutional capacity to ensure a common good for the population, such as food and nutritional security, remains critical, especially exacerbated by the global health crisis. However, it involves a multidimensionality of factors connecting various variables in a spiral, with the performance of health establishments as the coordinating axis developing initiatives to combat malnutrition in the country. Conclusions During the period 2010-2020 in Peru, the impact of public health expenditure on the reduction of malnutrition shows that in 10 departments, malnutrition was reduced; while in 14 departments, this indicator was not reduced. The most notable increases were in Pasco, where public health expenditure led to an increase in malnutrition by 17.61%, in Arequipa by 15.18%, in Lima by 9.85%, and in Cajamarca by 8.53%. Public health expenditure has a negative relationship with malnutrition, while the unemployment rate shows a positive relationship with malnutrition, as being unemployed leads to a higher cause of malnutrition in the population due to lower income. Malnutrition has a negative effect on public health expenditure, as little importance is given to public malnutrition policies, and public budgets are prioritized for other sectors. Education has a negative effect on the rural population, mainly explained by the trend that as the human capital of the population increases, there is a migration from rural to urban areas, moving to central zones to improve their income levels. Malnutrition has a positive effect on unemployment, as do education and inflation. High levels of malnutrition generate high unemployment rates, while the positive relationship between education and unemployment is explained by the excess accumulation of human capital exceeding job opportunities. The positive relationship between inflation and unemployment reflects the loss of purchasing power and delayed investment due to price fluctuations, leading to a decline in business confidence. Exports have had a positive effect on GDP during the analyzed period, as higher exports result in a positive balance of payments, impacting foreign exchange and terms of trade, fostering GDP growth. Conversely, unemployment shows a negative relationship with GDP due to the still high unemployment rates in Peru, which negatively affect economic growth by reducing private investment and capital attraction. Inflation also shows a negative relationship with GDP, as the inflationary spiral leads to a loss of purchasing power, consequently affecting economic growth. Data availability statement Zenodo. Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis. 10.5281/zenodo.12736705 ( Castro et al., 2024 ). This project contains the following underlying data: • Final results Panel data.xlsx This project contains the following extended data: • Stata Commands.pdf • Figure 1.Impact of Public Health Expenditure on Malnutrition by Department.png • Figure 2.Dynamics of the Identified Structural Equations in the Departments of Peru.jpg • Rectoral Resolution N° 760-2007_UCV_Code Of Ethics (1).pdf • STROBE_checklist_v4_combined.pdf Creative Commons Zero v1.0 Universal (CC0 License) References Antelo M, Magdalena P, Reboredo JC: Economic crisis and the unemployment effect on household food expenditure: The case of Spain. Food Policy. 2017; 69 : 11–24. Publisher Full Text Beltrán A, Castro JF: Modelos de datos de panel y variables dependientes limitadas: Un enfoque empírico. InUniversidad Del Pacifico (Ed.), Centro de Investigación Universidad del Pacífico. Biblioteca Nacional del Perú; 2010. 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Reference Source Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 02 Sep 2024 ADD YOUR COMMENT Comment Author details Author details 1 Universidad Cesar Vallejo, Avenida Víctor Larco, Trujillo, La Libertad, 1700, Peru 2 Universidad Nacional Pedro Ruiz Gallo, Juan XXIII 391, Lambayeque, 14013, Peru Percy Junior Castro Mejía Roles: Methodology, Writing – Review & Editing Rogger Orlando Morán-Santamaría Roles: Investigation, Methodology, Writing – Original Draft Preparation Yefferson Llonto Caicedo Roles: Investigation, Methodology, Writing – Original Draft Preparation Francisco Eduardo Cúneo Fernández Roles: Validation, Writing – Original Draft Preparation Nikolays Pedro Lizana Guevara Roles: Software, Visualization Milagros Judith Pérez Pérez Roles: Supervision, Visualization Lindon Vela Meléndez Roles: Methodology, Supervision, Visualization Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 17 Jan 2025, 13:990 https://doi.org/10.12688/f1000research.153477.3 version 2 Revised Published: 28 Oct 2024, 13:990 https://doi.org/10.12688/f1000research.153477.2 version 1 Published: 02 Sep 2024, 13:990 https://doi.org/10.12688/f1000research.153477.1 Copyright © 2025 Castro Mejía PJ et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Castro Mejía PJ, Morán-Santamaría RO, Llonto Caicedo Y et al. Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.12688/f1000research.153477.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 17 Jan 2025 Revised Views 0 Cite How to cite this report: Mauludyani AVR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r375314 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-375314 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Apr 2025 Anna Vipta Resti Mauludyani , IPB University, Bogor, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176735.r375314 This paper is expected to provide a comprehensive point of view on the relationship between public health expenditure and malnutrition. However, the discussion still needs to be elaborated more, especially on the impact of the public health ... Continue reading READ ALL This paper is expected to provide a comprehensive point of view on the relationship between public health expenditure and malnutrition. However, the discussion still needs to be elaborated more, especially on the impact of the public health expenditure on malnutrition, which was also not found in the identified structural equations. Add also implications of the results, particularly more specific programs to address malnutrition problem. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Community nutrition, nutrition policy analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mauludyani AVR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r375314 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-375314 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Wisnuwardani RW. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r377867 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-377867 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Apr 2025 Ratih Wirapuspita Wisnuwardani , Department of Public Health Nutrition, Faculty of Public Health, Mulawarman University, Samarinda, East Kalimantan, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176735.r377867 Percy Junior Castro Mejía et al. conducted a study on the Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020 , which can contribute to malnutrition prevention programs in Peru. This article will be very interesting for ... Continue reading READ ALL Percy Junior Castro Mejía et al. conducted a study on the Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020 , which can contribute to malnutrition prevention programs in Peru. This article will be very interesting for F1000 readers, as all countries are currently working to address nutrition issues. However, there are a few areas that need improvement, especially in data analysis and presentation of results. Tables 2-5: avoid using all tables. Using graphs would make it easier for readers to understand. Figure 2: Please simplify it, as it is very difficult to comprehend. The discussion is still not detailed enough and should compare with previous articles in meta-analysis and systematic reviews. The conclusion is too long; it should be shortened to a brief conclusion with recommendations. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Public health nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wisnuwardani RW. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r377867 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-377867 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 28 Oct 2024 Revised Views 0 Cite How to cite this report: Seferidi P. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r339501 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-339501 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Dec 2024 Paraskevi Seferidi , Imperial College London, London, UK Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.173076.r339501 This paper explores associations between public health spending and malnutrition in Peru. There are some major concerns regarding the paper. Below are a few specific comments and recommendations: Overall paragraph structure needs to be improved. ... Continue reading READ ALL This paper explores associations between public health spending and malnutrition in Peru. There are some major concerns regarding the paper. Below are a few specific comments and recommendations: Overall paragraph structure needs to be improved. In the introduction, the authors are using small paragraphs of 1-2 sentences that provide unlinked information on various issues that are not connected. Try to restructure your introduction into 3-4 paragraphs, one per issue explored, e.g. one on the issue of hunger and undernutrition, one on the issue of inadequate health expenditure, and one on the association between health expenditure and malnutrition. I'd recommend skipping the literature review subheading and just summarise the existing evidence base pertaining to your research question into one comprehensive paragraph. Make sure that you only include relevant literature about associations of interest. For example, there is a lot of detail on specific papers that seem a bit irrelevant (e.g. Katoch, 2024). A lot of the literature cited is about associations with health and not malnutrition more specifically, but there is no clear distinction between the two (e.g. Edney, 2018). Description of several papers is unclear (e.g. Bernet 2018, Calva 2021) - writing needs to be improved to better communicate what these papers have found. The authors should provide a clear research gap. Currently they argue that there is plenty of literature looking at associations between public health spending and malnutrition (first paragraph of methods). So what is the rationale of conducting this work? Also, the authors have not provided a clear aim and/or objectives in their introduction. There is a lot of repetition in the methods, so a lot of work needs to be done to improve this. Methods seem to be misleading. Initially, the authors argue that the sample size is 2112 observations (not sure if this is households?), but then from the equations it looks like the level of observation is department. So, it looks like this is an ecological analysis with a sample size of 24. But then later on it seems that the authors may have done multi-level analysis, which also seems to be confirmed by the tables in the results. Overall, I find it impossible to assess the statistical analysis based on the text provided by the authors. I have concerns about the definition of malnutrition using a binary measure of being above or below a cut-off point for energy intake. The authors try to explain how they estimated this, but I’m afraid it is not clear what the purpose of the methodology by Herrera is and if it is appropriately used here. I would be cautious with naming this variable malnutrition, as this is probably the % of households that have (per capita??) energy intake below 2318 kcal. The interpretation of the results would be much different in this case. In the results, it looks like the authors explored associations by department, but it was not clear from the methods what was the methodological approach to estimate these. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: public health nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Seferidi P. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r339501 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-339501 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 08 Feb 2025 Rogger Orlando Morán Santamaría , Universidad Cesar Vallejo, Avenida Víctor Larco, Trujillo, 1700, Peru 08 Feb 2025 Author Response To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of ... Continue reading To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of our finalised research. I am writing to inform you that the report has been uploaded with the suggested changes and that we will be able to continue with the process of publishing the article to the highest standards of quality. We hope we have met your requirements. We will await the new revision. Thank you very much. To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of our finalised research. I am writing to inform you that the report has been uploaded with the suggested changes and that we will be able to continue with the process of publishing the article to the highest standards of quality. We hope we have met your requirements. We will await the new revision. Thank you very much. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 08 Feb 2025 Rogger Orlando Morán Santamaría , Universidad Cesar Vallejo, Avenida Víctor Larco, Trujillo, 1700, Peru 08 Feb 2025 Author Response To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of ... Continue reading To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of our finalised research. I am writing to inform you that the report has been uploaded with the suggested changes and that we will be able to continue with the process of publishing the article to the highest standards of quality. We hope we have met your requirements. We will await the new revision. Thank you very much. To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of our finalised research. I am writing to inform you that the report has been uploaded with the suggested changes and that we will be able to continue with the process of publishing the article to the highest standards of quality. We hope we have met your requirements. We will await the new revision. Thank you very much. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Katoch OR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r335578 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-335578 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 31 Oct 2024 Om Raj Katoch , Department of Economics, Govt. Maulana Azad Memorial College Cluster University of Jammu, Jammu & Kashmir., Jammu & Kashmir, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.173076.r335578 The authors have revised the paper based on the comments, and the ... Continue reading READ ALL The authors have revised the paper based on the comments, and the quality has now been enhanced. In my opinion, it can be indexed now. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Katoch OR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r335578 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-335578 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 02 Sep 2024 Views 0 Cite How to cite this report: Katoch OR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.168387.r321092 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v1#referee-response-321092 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Sep 2024 Om Raj Katoch , Department of Economics, Govt. Maulana Azad Memorial College Cluster University of Jammu, Jammu & Kashmir., Jammu & Kashmir, India Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.168387.r321092 Review Report on the Research Paper: "Impact of Public Health Expenditure on Malnutrition Among Peruvians During the Period 2010-2020" I have reviewed your research paper and would like to provide feedback to help you enhance the quality ... Continue reading READ ALL Review Report on the Research Paper: "Impact of Public Health Expenditure on Malnutrition Among Peruvians During the Period 2010-2020" I have reviewed your research paper and would like to provide feedback to help you enhance the quality and clarity of your work. Please consider the following points as you revise your manuscript: 1. Background and Literature Review The topic you address is highly relevant and significant in the field of public health. However, I recommend that you expand the literature review to include more recent studies. This will help you establish a stronger foundation for your research question and contextualize your findings within the current body of knowledge. Incorporating recent literature will also demonstrate the relevance and timeliness of your study. Cite these articles: Om Raj Katoch (2022 [Ref - 1]) https://doi.org/10.1016/j.nut.2021.111565 Om Raj Katoch et al. (2024 [Ref - 2]) https://doi.org/10.1108/IJESM-10-2022-0007 Om Raj Katoch (2024 [Ref - 3]) https://doi.org/10.1108/NFS-03-2023-0055 2. Methodology The methodology section requires further elaboration to improve both clarity and comprehensibility. While the current draft mentions the use of structural equations and regression analysis, it is essential to provide detailed explanations to support the robustness of your approach. Specifically, you should clearly specify the data sources used in your analysis, discussing their reliability and relevance to the study. Additionally, clarifying the sample size and justifying its adequacy for the conducted analysis is crucial to ensure the validity of your results. Moreover, a detailed description of the specific statistical techniques employed, along with an explanation of why these techniques were chosen, will help readers understand the analytical framework. It is also important to offer a clearer explanation of how control variables were selected and discuss their relevance to the model. Finally, addressing the assumptions made during the analysis and explaining how potential biases were mitigated will further enhance the methodological rigor of your paper. By incorporating these elements, you will provide a more comprehensive and transparent account of your research methodology. 3. Policy Implications The policy implications you discuss are crucial and well-founded. However, to make this section more impactful, consider providing more concrete recommendations for policymakers. For instance, elaborate on how increased investment in health services can be implemented effectively, particularly in rural areas. Additionally, consider discussing how these findings might be applied in other developing countries facing similar malnutrition challenges, thereby broadening the applicability of your research. 4. Grammatical and Formatting Errors The manuscript contains several grammatical errors and awkward phrasings that need to be addressed. A thorough proofreading is essential to correct these issues and ensure that your paper meets academic standards for publication and indexing. Additionally, pay attention to consistent formatting throughout the manuscript, including citation styles and headings Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Katoch OR: Determinants of malnutrition among children: A systematic review. Nutrition . 2022; 96 : 111565 PubMed Abstract | Publisher Full Text 2. Katoch O, Sharma R, Parihar S, Nawaz A: Energy poverty and its impacts on health and education: a systematic review. International Journal of Energy Sector Management . 2024; 18 (2): 411-431 Publisher Full Text 3. Katoch O: Tackling child malnutrition and food security: assessing progress, challenges, and policies in achieving SDG 2 in India. Nutrition & Food Science . 2024. Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Katoch OR. Reviewer Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.168387.r321092 ) The direct URL for this report is: https://f1000research.com/articles/13-990/v1#referee-response-321092 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 02 Sep 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 17 Jan 25 read read Version 2 (revision) 28 Oct 24 read read Version 1 02 Sep 24 read Om Raj Katoch , Govt. Maulana Azad Memorial College Cluster University of Jammu, Jammu & Kashmir., Jammu & Kashmir, India Paraskevi Seferidi , Imperial College London, London, UK Ratih Wirapuspita Wisnuwardani , Mulawarman University, Samarinda, Indonesia Anna Vipta Resti Mauludyani , IPB University, Bogor, Indonesia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Mauludyani A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Apr 2025 | for Version 3 Anna Vipta Resti Mauludyani , IPB University, Bogor, Indonesia 0 Views copyright © 2025 Mauludyani A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper is expected to provide a comprehensive point of view on the relationship between public health expenditure and malnutrition. However, the discussion still needs to be elaborated more, especially on the impact of the public health expenditure on malnutrition, which was also not found in the identified structural equations. Add also implications of the results, particularly more specific programs to address malnutrition problem. Competing Interests No competing interests were disclosed. Reviewer Expertise Community nutrition, nutrition policy analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Mauludyani AVR. Peer Review Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r375314) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-375314 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wisnuwardani R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Apr 2025 | for Version 3 Ratih Wirapuspita Wisnuwardani , Department of Public Health Nutrition, Faculty of Public Health, Mulawarman University, Samarinda, East Kalimantan, Indonesia 0 Views copyright © 2025 Wisnuwardani R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Percy Junior Castro Mejía et al. conducted a study on the Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020 , which can contribute to malnutrition prevention programs in Peru. This article will be very interesting for F1000 readers, as all countries are currently working to address nutrition issues. However, there are a few areas that need improvement, especially in data analysis and presentation of results. Tables 2-5: avoid using all tables. Using graphs would make it easier for readers to understand. Figure 2: Please simplify it, as it is very difficult to comprehend. The discussion is still not detailed enough and should compare with previous articles in meta-analysis and systematic reviews. The conclusion is too long; it should be shortened to a brief conclusion with recommendations. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Public health nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Wisnuwardani RW. Peer Review Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.176735.r377867) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-990/v3#referee-response-377867 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Seferidi P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Dec 2024 | for Version 2 Paraskevi Seferidi , Imperial College London, London, UK 0 Views copyright © 2024 Seferidi P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper explores associations between public health spending and malnutrition in Peru. There are some major concerns regarding the paper. Below are a few specific comments and recommendations: Overall paragraph structure needs to be improved. In the introduction, the authors are using small paragraphs of 1-2 sentences that provide unlinked information on various issues that are not connected. Try to restructure your introduction into 3-4 paragraphs, one per issue explored, e.g. one on the issue of hunger and undernutrition, one on the issue of inadequate health expenditure, and one on the association between health expenditure and malnutrition. I'd recommend skipping the literature review subheading and just summarise the existing evidence base pertaining to your research question into one comprehensive paragraph. Make sure that you only include relevant literature about associations of interest. For example, there is a lot of detail on specific papers that seem a bit irrelevant (e.g. Katoch, 2024). A lot of the literature cited is about associations with health and not malnutrition more specifically, but there is no clear distinction between the two (e.g. Edney, 2018). Description of several papers is unclear (e.g. Bernet 2018, Calva 2021) - writing needs to be improved to better communicate what these papers have found. The authors should provide a clear research gap. Currently they argue that there is plenty of literature looking at associations between public health spending and malnutrition (first paragraph of methods). So what is the rationale of conducting this work? Also, the authors have not provided a clear aim and/or objectives in their introduction. There is a lot of repetition in the methods, so a lot of work needs to be done to improve this. Methods seem to be misleading. Initially, the authors argue that the sample size is 2112 observations (not sure if this is households?), but then from the equations it looks like the level of observation is department. So, it looks like this is an ecological analysis with a sample size of 24. But then later on it seems that the authors may have done multi-level analysis, which also seems to be confirmed by the tables in the results. Overall, I find it impossible to assess the statistical analysis based on the text provided by the authors. I have concerns about the definition of malnutrition using a binary measure of being above or below a cut-off point for energy intake. The authors try to explain how they estimated this, but I’m afraid it is not clear what the purpose of the methodology by Herrera is and if it is appropriately used here. I would be cautious with naming this variable malnutrition, as this is probably the % of households that have (per capita??) energy intake below 2318 kcal. The interpretation of the results would be much different in this case. In the results, it looks like the authors explored associations by department, but it was not clear from the methods what was the methodological approach to estimate these. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise public health nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 08 Feb 2025 Rogger Orlando Morán Santamaría, Universidad Cesar Vallejo, Avenida Víctor Larco, Trujillo, 1700, Peru To Paraskevi Seferidi Imperial College London, London, UK Please accept greetings from the authors of the article. We appreciate your comments and know that they improve the quality of our finalised research. I am writing to inform you that the report has been uploaded with the suggested changes and that we will be able to continue with the process of publishing the article to the highest standards of quality. We hope we have met your requirements. We will await the new revision. Thank you very much. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Seferidi P. Peer Review Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r339501) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-339501 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Katoch O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 31 Oct 2024 | for Version 2 Om Raj Katoch , Department of Economics, Govt. Maulana Azad Memorial College Cluster University of Jammu, Jammu & Kashmir., Jammu & Kashmir, India 0 Views copyright © 2024 Katoch O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have revised the paper based on the comments, and the quality has now been enhanced. In my opinion, it can be indexed now. Competing Interests No competing interests were disclosed. Reviewer Expertise Nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Katoch OR. Peer Review Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.173076.r335578) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-990/v2#referee-response-335578 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Katoch O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Sep 2024 | for Version 1 Om Raj Katoch , Department of Economics, Govt. Maulana Azad Memorial College Cluster University of Jammu, Jammu & Kashmir., Jammu & Kashmir, India 0 Views copyright © 2024 Katoch O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Review Report on the Research Paper: "Impact of Public Health Expenditure on Malnutrition Among Peruvians During the Period 2010-2020" I have reviewed your research paper and would like to provide feedback to help you enhance the quality and clarity of your work. Please consider the following points as you revise your manuscript: 1. Background and Literature Review The topic you address is highly relevant and significant in the field of public health. However, I recommend that you expand the literature review to include more recent studies. This will help you establish a stronger foundation for your research question and contextualize your findings within the current body of knowledge. Incorporating recent literature will also demonstrate the relevance and timeliness of your study. Cite these articles: Om Raj Katoch (2022 [Ref - 1]) https://doi.org/10.1016/j.nut.2021.111565 Om Raj Katoch et al. (2024 [Ref - 2]) https://doi.org/10.1108/IJESM-10-2022-0007 Om Raj Katoch (2024 [Ref - 3]) https://doi.org/10.1108/NFS-03-2023-0055 2. Methodology The methodology section requires further elaboration to improve both clarity and comprehensibility. While the current draft mentions the use of structural equations and regression analysis, it is essential to provide detailed explanations to support the robustness of your approach. Specifically, you should clearly specify the data sources used in your analysis, discussing their reliability and relevance to the study. Additionally, clarifying the sample size and justifying its adequacy for the conducted analysis is crucial to ensure the validity of your results. Moreover, a detailed description of the specific statistical techniques employed, along with an explanation of why these techniques were chosen, will help readers understand the analytical framework. It is also important to offer a clearer explanation of how control variables were selected and discuss their relevance to the model. Finally, addressing the assumptions made during the analysis and explaining how potential biases were mitigated will further enhance the methodological rigor of your paper. By incorporating these elements, you will provide a more comprehensive and transparent account of your research methodology. 3. Policy Implications The policy implications you discuss are crucial and well-founded. However, to make this section more impactful, consider providing more concrete recommendations for policymakers. For instance, elaborate on how increased investment in health services can be implemented effectively, particularly in rural areas. Additionally, consider discussing how these findings might be applied in other developing countries facing similar malnutrition challenges, thereby broadening the applicability of your research. 4. Grammatical and Formatting Errors The manuscript contains several grammatical errors and awkward phrasings that need to be addressed. A thorough proofreading is essential to correct these issues and ensure that your paper meets academic standards for publication and indexing. Additionally, pay attention to consistent formatting throughout the manuscript, including citation styles and headings Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Katoch OR: Determinants of malnutrition among children: A systematic review. Nutrition . 2022; 96 : 111565 PubMed Abstract | Publisher Full Text 2. Katoch O, Sharma R, Parihar S, Nawaz A: Energy poverty and its impacts on health and education: a systematic review. International Journal of Energy Sector Management . 2024; 18 (2): 411-431 Publisher Full Text 3. Katoch O: Tackling child malnutrition and food security: assessing progress, challenges, and policies in achieving SDG 2 in India. Nutrition & Food Science . 2024. Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Nutrition I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Katoch OR. Peer Review Report For: Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :990 ( https://doi.org/10.5256/f1000research.168387.r321092) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-990/v1#referee-response-321092 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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