The total factor productivity growth of health systems in African least developed countries

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This is even more urgent in African Least Developed Countries (African LDCs) that need to improve the health of their populations. Thus, the objective of this study is to assess the total factor productivity growth of health systems of 29 African Least Developed Countries for the 2008-2018 period. Methods: The study uses data from the World Bank and the World Health Organization. Using the Data Envelopment Analysis (DEA) Malmquist index, the inputs that were used in the study included domestic general government health expenditure, domestic private health expenditure, external health expenditure and out of pocket health expenditure while the outputs were life expectancy at birth, maternal mortality rate, under five mortality rate, and infant mortality rate. Results: Sixteen African LDCs registered progress in the total factor productivity growth of their health systems while thirteen registered a decline the total factor productivity growth of their health systems. Overall, there was 0.3% average increase in total factor productivity growth of health systems in African LDCs. This was attributed to a 1.2% increase in technical efficiency change and a 0.9% average decrease in the technical change of health systems in African LDCs. Conclusions: African LDCs with less productive health systems are advised to bench mark the policies of African LDCs with productive health systems." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/12-1050/v1", "name": "The total factor productivity growth of health systems in African..." } } ] } Home Browse The total factor productivity growth of health systems in African... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Musoke E, Lule Yawe B and Ddumba Ssentamu J. The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.12688/f1000research.135418.1 ) 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 The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] Edward Musoke https://orcid.org/0000-0002-6337-1007 1 , Bruno Lule Yawe 1 , John Ddumba Ssentamu 1 Edward Musoke https://orcid.org/0000-0002-6337-1007 1 , Bruno Lule Yawe 1 , John Ddumba Ssentamu 1 PUBLISHED 29 Aug 2023 Author details Author details 1 College of Business and Management Sciences, Makerere University, Kampala, Uganda Edward Musoke Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Bruno Lule Yawe Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing John Ddumba Ssentamu Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Global Public Health gateway. This article is included in the Health Services gateway. Abstract Background: Given the several health policy reforms in various countries in the Sustainable Development Goals (SDGs) era, the need for efficient and productive health systems has become of great concern. This is even more urgent in African Least Developed Countries (African LDCs) that need to improve the health of their populations. Thus, the objective of this study is to assess the total factor productivity growth of health systems of 29 African Least Developed Countries for the 2008-2018 period. Methods: The study uses data from the World Bank and the World Health Organization. Using the Data Envelopment Analysis (DEA) Malmquist index, the inputs that were used in the study included domestic general government health expenditure, domestic private health expenditure, external health expenditure and out of pocket health expenditure while the outputs were life expectancy at birth, maternal mortality rate, under five mortality rate, and infant mortality rate. Results: Sixteen African LDCs registered progress in the total factor productivity growth of their health systems while thirteen registered a decline the total factor productivity growth of their health systems. Overall, there was 0.3% average increase in total factor productivity growth of health systems in African LDCs. This was attributed to a 1.2% increase in technical efficiency change and a 0.9% average decrease in the technical change of health systems in African LDCs. Conclusions: African LDCs with less productive health systems are advised to bench mark the policies of African LDCs with productive health systems. READ ALL READ LESS Keywords Total factor productivity growth, technical efficiency, Data Envelopment Analysis Malmquist Index, Health Systems, African Least Developed Countries Corresponding Author(s) Edward Musoke ( [email protected] ) Bruno Lule Yawe ( [email protected] ) John Ddumba Ssentamu ( [email protected] ) Close Corresponding authors: Edward Musoke, Bruno Lule Yawe, John Ddumba Ssentamu Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2023 Musoke E 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: Musoke E, Lule Yawe B and Ddumba Ssentamu J. The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.12688/f1000research.135418.1 ) First published: 29 Aug 2023, 12 :1050 ( https://doi.org/10.12688/f1000research.135418.1 ) Latest published: 29 Aug 2023, 12 :1050 ( https://doi.org/10.12688/f1000research.135418.1 ) Introduction Developed and Least Developed Countries (LDCs) have a clear, pressing, and ongoing need for a healthy population and productive health system ( Mohamadi et al ., 2020 ). This according to Kim et al . (2016) has prompted health sector reforms to raise health systems performance in these countries. Key amongst these reforms indicating a country’s commitment to the health of its citizens is health expenditure ( Kim et al ., 2016 ). According to Anaemene (2018) , unlike developed countries that have high levels of health expenditure, least developed countries and in particular African LDCs represent 1% of global health spending and are responsible for 25% of the world’s disease burden which accounts for 60% of their deaths ( United Nations, 2017 ). In light of this Ojwang’Oyieke and Karamagi (2023) state that without efficient and productive use of scarce health resources, these health challenges could potentially overwhelm the health systems of African LDCs. Thus estimating the total factor productivity growth of health systems of African LDCs which is the maximization of outputs for a given level of scarce inputs, is critical. Thus, the general objective of this study is to assess the total factor productivity growth of health systems of 29 African LDCs for the 2008-2018 period. The findings in this paper are particularly important for a number of reasons. First, African LDCs with less productive health systems can benchmark practices of African LDCs with productive health systems. Second, it adds to the small but growing body of knowledge about the total factor productivity growth of health systems in resource-constrained settings. Future researchers might find use for such important literature. The rest of this paper is organized as follows: the literature review is presented in the next section followed by the methodology and discussion of findings. After the discussion of findings, the conclusions and policy recommendations are presented followed by acknowledgements, statement on data availability, declaration of competing interests, grant information and the acknowledgement. Literature review Theoretical literature To comprehend the total factor productivity growth of health systems in African LDCs, the theory of constraints by Goldratt and Cox (1984) is adopted. According to Ochiel (2019) , the premise of this theory is that the transformation of inputs into outputs is through the production process is faced with constraints. Thus the elimination of these constraints is the ultimate goal of each health systems to witness progress in their total factor productivity growth ( Aguilar-Escobar & Garrido-Vega, 2016 ; Ochiel, 2019 ). This is why Ochiel (2019) suggests that several steps like investing in technology can be adopted to counteract the negative effects of the constraints in health systems and witness progress in the total factor productivity growth of these health systems. Empirical evidence Several studies have assessed the total factor productivity growth of health systems comparing several countries from different regions of the world like Organization for Economic Co-operation and Development (OECD) countries ( Adang & Borm, 2007 ; Kim et al ., 2016 ), European and Central Asian countries ( Hsu, 2014 ), Visegrád group countries ( Grausová et al ., 2014 ), Continental African Countries ( Kirigia et al ., 2007 ), World Health Organization countries from the Eastern Mediterranean region ( Masri & Asbu, 2018 ), Upper Middle Income Countries with focus on Iran ( Mohamadi et al ., 2020 ), countries from the Association of South East Asian Nations ( Singh et al ., 2021 ) and developed countries ( Almessabi, 2020 ). They used several inputs like number of medical personnel ( Adang & Borm, 2007 ; Almessabi, 2020 ), health expenditure ( Almessabi, 2020 ; Kim et al ., 2016 ; Masri & Asbu, 2018 ), number of hospital beds ( Almessabi, 2020 ; Grausová et al ., 2014 ), education ( Kim et al ., 2016 ; Kirigia et al ., 2007 ). Several outputs like infant mortality rate ( Almessabi, 2020 ; Kim et al ., 2016 ; Masri & Asbu, 2018 ), under five mortality rate ( Almessabi, 2020 ), life expectancy ( Adang & Borm, 2007 ; Grausová et al ., 2014 ; Hsu, 2014 ) and maternal mortality ratio ( Ibrahim et al ., 2019 ) have been used as well. All these studies employed the Data Envelopment Analysis (DEA) based Malmquist index and established variations in the total factor productivity growth of their health systems, with health systems of some countries experiencing a regress in productivity ( Almessabi, 2020 ; Hsu, 2014 ; Masri & Asbu, 2018 ) while others demonstrating progress in productivity ( Adang & Borm, 2007 ; Kim et al ., 2016 ; Kirigia et al ., 2007 ). The regress in productivity was attributed increase or decrease in efficiency ( Almessabi, 2020 ; Hsu, 2014 ; Masri & Asbu, 2018 ) and an increase or decrease in technology ( Almessabi, 2020 ; Hsu, 2014 ; Masri & Asbu, 2018 ). Similarly, the progress in productivity was attributed to an increase or decrease in efficiency ( Adang & Borm, 2007 ; Kim et al ., 2016 ; Kirigia et al ., 2007 ) and an increase or decrease in technology ( Adang & Borm, 2007 ; Kim et al ., 2016 ; Kirigia et al ., 2007 ). The contribution and originality of this paper is based on Sajadi et al . (2020) suggestion of selecting the best input and output combinations which is so crucial in the estimation of the total factor productivity growth of health systems. According to Wagner and Shimshak (2007) , most of the studies assessing the total factor productivity growth of health systems consider the input and output combinations as simply “givens” based on literature and do not focus on the choice of the best input and output combinations. Yet if the choice of input and output combinations is not given the attention it deserves, results of total factor productivity growth are biased and inconsistent. Adang and Borm (2007) further note that much of the critique of the 2000 World Health Report from the World Health Organization (2000) had to do with completeness of the production function and the choice of the inputs and output combinations. This study addresses this gap by using correlational analysis as used by Rooijakkers (2018) ; Kizza (2012) ; Hisali and Yawe (2011) and Yawe (2006) to select the best input and output combinations for the estimation of the total factor productivity growth of health systems. Methods Unit of analysis and variables Following Kizza (2012) and Yawe (2006) each African LDCs is considered to be a Decision Making Unit (DMU) or unit of analysis. Twenty-nine African LDCs are considered for this study based on the availability of data (see Table 1 ). According to Table 1 , of the twenty-nine African LDCs, twelve are found in west Africa, eight are found in east Africa, six are found in south Africa and three in central Africa. According to Wale-Oshinowo et al . (2022) , the geographic configurations resulting from the colonial and post-colonial delineation of these regions of Africa are to blame for the high proportion of African LDCs in West and East Africa. Table 1. Regional distribution of African least developed countries. Region Countries 1 Central Africa Central African Republic, Chad, Democratic Republic of Congo 2 East Africa Burundi, Djibouti, Eritrea, Ethiopia, Rwanda, Sudan, Tanzania, Uganda 3 West Africa Benin, Burkina Faso, Gambia, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Senegal, Sierra Leone, Togo 4 Southern Africa Angola, Lesotho, Madagascar, Malawi, Mozambique, Zambia Based on studies like: Hadad et al . (2013) ; Çelik et al . (2017) : Ibrahim et al . (2019) ; Masri and Asbu (2018) ; Behr and Theune (2017) ; Retzlaff-Roberts et al . (2004) and Mohamadi et al . (2020) , four inputs and outputs are considered for this study. Since the production of health at a macro level is complicated, health outcomes are used as health outputs ( Çelik et al ., 2017 ; Ng, 2008 ; Peacock et al ., 2001 ). The input, output data and their definitions based on the World Bank (2021) and World Health Organization (2019) are shown in Table 2 . Table 2. Input and output variables used in the study. No Variable Definition Source of definition Inputs 1 Domestic General Government Health Expenditure This is the public expenditure on health from domestic sources per capita expressed in current USD. World Health Organization (2019) 2 Out of Pocket Health Expenditure This is health expenditure through out-of-pocket payments per capita in USD. Out of pocket payments are spending on health directly out of pocket by households in each country. World Health Organization (2019) 3 Domestic Private Health Expenditure This is the current private expenditures on health per capita expressed in current USD. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers. World Health Organization (2019) 4 External Health Expenditure This is the current external expenditure on health per capita expressed in current USD. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. World Health Organization (2019) Outputs 1 Life Expectancy at Birth Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. World Bank (2021) 2 Maternal Mortality Ratio The maternal mortality ratio is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth. World Bank (2021) 3 Under five Mortality Rate The probability of a child born in a specific year or period dying before reaching the age of five, if subject to age-specific mortality rates of that period World Bank (2021) 4 Infant Mortality Rate Infant mortality rate is the probability of a child born in a specific year or period dying before reaching the age of one, if subject to age-specific mortality rates of that period. World Bank (2021) The estimation of the total factor productivity growth of health systems require the use of output variables that capture good health outcomes. To conform to isotonicity and devise output variables that capture good health outcomes in infants, mothers and children under five years ( Ibrahim et al ., 2019 ; Zhou et al ., 2020 ). The Infant Mortality Rate (IMR); Maternal Mortality Ratio (MMR) and under-five mortality rate (U5MR) values are converted to infant survival rate (ISR) ( ISR = 1,000 − IMR / IMR ), maternal survival ratio (MSR) ( MSR = 100,000 − MMR / MMR ) and under five survival rate (U5SR) ( U 5 SR = 1,000 − U 5 SR / U 5 SR ). Choice of the best input and output combinations Correlation analysis recommended by Cetin and Bahce (2016) ; Yawe (2006) and Kizza (2012) is used to select the best input and output combinations. According to Cetin and Bahce (2016) , input and output combinations that are highly correlated and significant are redundant and dropped from the further analysis of the total factor productivity growth of health systems. Furthermore, following Kizza (2012) and Yawe (2006) input and output combinations that provide the highest average total factor productivity growth are chosen for the DEA based Malmquist model. Test for endogeneity for the most preferred model According to Santín and Sicilia (2017) and Cordero et al . (2013) endogeneity occurs when the technical efficiency scores are strongly correlated with any one input. Correlation analysis suggested by Dhaoui (2019) is used to test for potential endogeneity in the assessment of the total factor productivity growth of health systems in African LDCs. Theoretical framework The theoretical framework for estimating the total factor productivity growth of health systems in African LDCs is based on Solow (1957) model which is summarized as: (1) Q t = A t F X t Y t Where Q t is the output and A t is the total factor productivity, which measures the shift in the production function at given the X , Y inputs and technology set. This total factor productivity ( A t ) is measured using a non-parametric index number ( Hulten, 1986 ). Following Ojwang’Oyieke and Karamagi (2023) , since this approach does not impose a specific form on the production function, equation (1) is converted to a (logarithmic) differential of the production function as: (2) Q ∗ Q t = ∂ Q ∂ X X t Q t X t ∗ Q t + ∂ Q ∂ + Y t ∗ Y t + A ∗ A t According to Hulten (1986) , the growth rate of real output can be factored out into the growth rate of X and Y inputs weighted by their output elasticities and the growth rate of the hicksian efficiency index. By total differentiation of equation (2), Solow (1957) showed that the hicksian efficiency index is a residual growth rate of output that is not accounted for by the growth in inputs which is given as: (3) R t = Q ∗ Q t − S t k = S t ∗ X t − S t l + Y t ∗ Y t = A t ∗ A t Where S t = x t y t : x t can produce y t Is the feasible technology set which contains a combination of x t inputs and x t outputs. Thus the solow residual R t = the hicksian index. Solow concluded that, theoretically, this growth rate was equal to the growth rate of the hicksian efficiency parameter A t ∗ A t ( Ojwang’Oyieke & Karamagi, 2023 ). Abramovitz (1956) called this residual as a measure of the degree of our ‘ignorance.’ “This ignorance could be wanted (technical, scale and technological innovation) or unwanted like (measurement errors, omitted variables, aggregation bias, and model misspecification)” ( Ojwang’Oyieke & Karamagi, 2023 ). For the case of African LDCs, assuming that the unwanted ignorance is minimal, and hence attribute the solow residual to technical, scale and technological innovation in the decomposition of total factor productivity growth ( Zofio, 2007 ). Empirical model Following Masri and Asbu (2018) , the output-oriented Variable Returns to Scale Data Envelopment Analysis (VRS-DEA) based Malmquist total factor productivity index is adopted for estimating the total factor productivity growth of health systems in African LDCs. The output-oriented VRS malmquist total factor productivity index is chosen over the input-oriented Constant Returns to Scale (CRS) approach because it is better suited for least developed countries while the input oriented is better suited for developed countries that have better health outputs ( Dhaoui, 2019 ; Dingake, 2017 ). If countries produce multiple outputs y t using multiple inputs x t , productivity change is measured using total factor productivity index also called multifactor productivity index. The output distance function each country over a given period of time is given as (4) D 0 t x t y t = inf θ : x t y t / θ ∈ T t where T t is the production set. Following Chowdhury et al . (2011) , defining the multifactor productivity index requires the definition of the distance function with respect to two different time periods, such as: (5) D 0 t x t + 1 y t + 1 = inf θ : x t + 1 y t + 1 / θ ∈ T t and (6) D 0 t + 1 x t y t = inf θ : x t y t / θ ∈ T t + 1 where T t and T t + 1 are the production sets in periods 1 and 2 respectively. The first distance function, in equation (5), measures the maximum proportional change in outputs required to make x t + 1 y t + 1 feasible in relation to the technology at the previous period t . Similarly, the second mixed-period distance function, equation (6), measures the maximum proportional change in output required to make x t y t feasible in relation to the technology at t + 1 which we call D 0 t + 1 x t y t . The Malmquist total factor productivity index (MTFP) measures total factor productivity (TFP) change between two time points in terms of ratios of distance functions. The MTFP between two time periods ( t and t + 1 ) using period t and period t + 1 technologies respectively is given as for period t (7) M 0 t = D 0 t x t + 1 y t + 1 D 0 t x t y t For period t + 1 (8) M 0 t + 1 = D 0 t + 1 x t + 1 y t + 1 D 0 t + 1 x t y t Where; M 0 t and M 0 t + 1 denote the MTFP in period t and t + 1 respectively; D 0 t x t + 1 y t + 1 refers to the output distance function which evaluates period t + 1 data relative to the technology in period t ; D 0 t x t y t is output distance function evaluating period t data relative to technology in period t ; D 0 t + 1 x t + 1 y t + 1 is the output distance function evaluating period t + 1 data relative to technology in period t + 1 ; D 0 t + 1 x t y t is the output distance function evaluating period t data relative to technology in period t + 1 ; Using period t and t + 1 technologies, the MTFP is defined as the geometric mean if the equations as follows; (9) M 0 t = x t + 1 y t + 1 x t y t = D 0 t x t + 1 y t + 1 D 0 t x t y t D 0 t + 1 x t + 1 y t + 1 D 0 t + 1 x t y t 1 2 The MTFP in equation (9) is further decomposed into efficiency change EFFCH and technical/technological TECHCH change as follows (10) M 0 t = x t + 1 y t + 1 x t y t = D 0 t + 1 x t + 1 y t + 1 D 0 t x t y t D 0 t x t + 1 y t + 1 D 0 t + 1 x t + 1 y t + 1 D 0 t x t y t D 0 t + 1 x t y t 1 2 That is: M 0 t = TEFFCH × TECHCH . The MTFP index value greater than 1 indicates growth in productivity, whereas a value less than 1 indicates a decline in productivity between periods t and t + 1 . A value of 1 denotes stagnation in productivity ( Kirigia et al ., 2007 ). Likewise for efficiency change and technical change, if TEFFCH > < then there is an increase (decrease) in efficiency and technical progress (regress). Data analysis The DEA based Malmquist model is estimated using DEAP version 2.1 a free DEA Program developed by Coelli (1996) . STATA version 15 by Stata Corp (2015) is used for the pre estimation techniques (choice of the best input/output combinations and checking for endogeneity issues regarding the total factor productivity growth of health systems). R , a free software environment for statistical computing and graphics, can be used for this analysis as well. Please see Underlying data ( Musoke et al ., 2023 ) for access to the specific datasets used in the study. Results and discussion Descriptive statistics There is variation among the chosen inputs and outputs for various Africa LDCs (see Table 3 ). Table 3. Descriptive statistics of the input and output variables (n = 29) from 2008–2018. Variable Observations Mean Std. Dev. Min Max Inputs Domestic General Government Health Expenditure 319 14.273 16.633 0.927 89.079 Domestic Private Health Expenditure 319 20.959 17.611 2.182 147.569 External Health Expenditure 319 11.774 9.137 1.121 74.705 Out of Pocket Health Expenditure 319 18.098 16.253 1.825 139.601 Outputs Under Five Survival Rate 319 0.013 0.007 0.005 0.046 Maternal Survival Ratio 319 1.438 1.587 -0.405 8.259 Life Expectancy at Birth 319 59.056 4.901 43.384 68.7 Infant Survival Rate 319 17.453 5.422 7.734 35.63 The minimum and maximum amounts for domestic general government health spending were 0.927 and 89.097 million US dollars, respectively, while the minimum and maximum amounts for external health spending were 1.121 and 74.705 million US dollars. The difference between domestic private health spending and out-of-pocket medical expenses is even greater, with minimum and maximum values of 2.182 and 1.825 million US dollars and 139.601 and 147.569 million US dollars, respectively. For the health outputs, the average life expectancy at birth is 59.056 years, with a range of 43.384 to 68.7 years. With minimum values of 0.005, -0.405, and 7.734 and maximum values of 0.046, 8.259, and 35.63, respectively, the average under-five survival rate, maternal survival ratio, and infant survival rate are 0.013, 1.438, and 17.453, respectively. Choice of input and output combinations To determine the interrelationships between various input and output variables, the Pearson’s correlation matrix for the input and output variables in Table 4 is calculated. Table 4. Pearson Correlation Matrix of Inputs and Output Variables (n = 29), 2008–2018. Under Five Survival Rate Maternal Survival Ratio Life Expectancy at Birth Infant Survival Rate Domestic General Government Health Expenditure Domestic Private Health Expenditure External Health Expenditure Out of Pocket Health Expenditure Under Five Survival Rate 1 Maternal Survival Ratio 0.837 *** 1 Life Expectancy at Birth 0.0660 0.118 * 1 Infant Survival Rate 0.0771 0.0760 0.775 *** 1 Domestic General Government Health Expenditure -0.0820 0.117 * -0.0772 -0.0474 1 Domestic Private Health Expenditure 0.187 *** 0.190 *** 0.0602 -0.0832 0.357 *** 1 External Health Expenditure 0.404 *** 0.442 *** -0.113 * 0.0689 0.0658 -0.0780 1 Out of Pocket Health Expenditure 0.168 ** 0.122 * 0.0411 -0.104 0.282 *** 0.980 *** -0.0911 1 * p < 0.05. ** p < 0.01. *** p < 0.001 indicates 5%. 1% and 0.1% significance level. DEA malmquist model specifications Several input/output combinations for three (3) DEA model specifications based on the output orientation and Variable Returns to Scale (VRS) assumption are presented in Table 5 . The results in Table 5 are in light of the results of the Pearson’s correlation matrix in Table 4 . Only two outputs and all inputs are included in the DEA Model 1. Under five survival rate and maternal survival rate are dropped from DEA Model 1 as outputs because they have a strong significant positive correlation r = 0.837 > 0.5 p < 0.001 . Table 5. DEA (Data Envelopment Analysis) Malmquist model specifications for different input/output combinations. Variables/Model 1 2 3 Inputs Domestic General Government Health Expenditure X X X Domestic Private Health Expenditure X X External Health Expenditure X X X Out of Pocket Health Expenditure X X Outputs Under Five Survival Rate X X Maternal Survival Ratio X X Life Expectancy at Birth X X Infant Survival Rate X X DEA malmquist model 2 has two outputs and all inputs. DEA malmquist model 2’s outputs life expectancy at birth and infant survival rate are dropped due to their strong significant positive correlation r = 0.775 > 0.5 p 0.5 p < 0.001 , domestic private health expenditure and out-of-pocket health expenditure inputs were dropped for DEA malmquist model 3. Results of the total factor productivity growth of the three estimated DEA Malmquist models based on the several input and output combinations for DEA malmquist model specifications in Table 5 are presented in Table 6 . Over the 2008-2018 period, the average total factor productivity changes in DEA malmquist model 1 is 0.990 indicating a 1% regress in productivity. Similarly, the average total factor productivity changes in DEA malmquist model 3 is 0.983 indicating a 1.7% regress in productivity. The average total factor productivity change in DEA malmquist model 2 is 1.003 which indicates a productivity progress of 0.3%. Table 6. Results of the total factor productivity growth for three (3) DEA malmquist models for (n = 29) African LDCs from 2008–2018. Country/DMU Model 1 Model 2 Model 3 tfpch tfpch tfpch Angola 1.029 1.062 1.059 Benin 1.011 1.022 1.006 Burkina Faso 0.980 1.005 0.991 Burundi 1.019 1.077 1.024 Central African Republic 0.951 1.007 0.960 Chad 0.988 1.003 0.987 Democratic Republic of Congo 0.973 0.99 0.965 Djibouti 0.942 0.966 0.933 Eritrea 1.022 1.018 1.030 Ethiopia 0.937 0.975 0.935 Gambia 1.056 1.049 1.076 Guinea 0.963 0.972 0.962 Guinea-Bissau 1.060 0.972 1.092 Lesotho 0.987 0.993 0.954 Liberia 0.938 0.933 0.925 Madagascar 1.036 1.084 1.008 Malawi 1.032 1.013 1.000 Mali 0.959 1.024 0.914 Mauritania 0.962 1.001 0.960 Mozambique 0.978 0.958 0.999 Niger 0.992 1.062 1.034 Rwanda 1.054 0.998 0.997 Senegal 0.955 0.959 0.967 Sierra Leone 0.890 0.925 0.896 Sudan 0.925 0.917 0.919 Togo 0.926 0.944 0.936 Uganda 1.073 1.075 1.056 Tanzania 1.028 1.037 1.002 Zambia 1.067 1.071 0.965 Mean 0.990 1.003 0.983 ≥1 12 16 11 <1 17 13 18 The preferred DEA malmquist model A comparison of all the three (3) DEA Malmquist models in Table 6 indicated that model 2 is the most preferred model with an average total factor productivity growth of 1.003 and 16 of 29 African LDCs on the frontier. Test for endogeneity for the most preferred DEA malmquist model 2 Results of the pearsons correlation between inputs and technical efficiency scores based on VRS for the most popular DEA Malmquist model 2, are presented in Table 7 . Table 7. Pearson correlation between inputs and Technical efficiency scores based on VRS (Variable Returns to Scale) of DEA Malmquist Model Two (2) of (n = 29) African LDCs from 2008 to 2018. Inputs Technical efficiency scores based on (VRS) Domestic General Government Health Expenditure -0.14464 Domestic Private Health Expenditure -0.10112 External Health Expenditure -0.16054 Out of Pocket Health Expenditure -0.13253 Since endogeneity typically denotes a strong correlation between inputs and the technical efficiency scores based on the VRS ( Orme & Smith, 1996 ). According to Table 7 , there isn’t much of a correlation between the input variables and technical efficiency scores. As a result, the DEA Malmquist Model 2 does not have an endogeneity issue and can be adopted for analysis. Malmquist index summary for annual means Results of the Malmquist index summary of annual means for DEA Malmquist Model 2 of the African LDCs from 2008 to 2018 are presented in Table 8 . According to Kizza (2012) and Yawe (2006) , the annual means of the Malmquist Index are geometric in nature and represent the efficiency change, technical change and total factor productivity change. Table 8. Malmquist index summary of annual means for DEA malmquist model 2 of (n = 29) African LDCs from 2008 to 2018. Year Technical efficiency change (effch) Technical change (techch) Pure efficiency change (pech) Scale efficiency change (sech) Total Factor productivity change (tfpch) 2010 * 1.072 0.934 0.968 1.107 1.001 2011 0.947 1.035 0.964 0.983 0.981 2012 0.945 1.002 0.961 0.983 0.947 2013 1.119 0.926 1.072 1.044 1.036 2014 1.012 0.973 1.076 0.94 0.985 2015 1.025 0.938 1.016 1.009 0.961 2016 1.055 1.074 1.049 1.006 1.134 2017 0.879 1.184 0.936 0.940 1.041 2018 1.076 0.886 1.050 1.025 0.953 Mean 1.012 0.991 1.009 1.003 1.003 * Note that 2010 refers to the change between 2008 and 2010. The findings in Table 8 demonstrate that over time, the average technical efficiency change of health systems in African LDCs improved by 1.2%, the average pure efficiency change of African LDCs’ health systems improved by 0.9%. The average scale efficiency change and total factor productivity change improved by 0.3%. The mean pure efficiency change and mean scale efficiency change were responsible for the 1.2% increase in the average technical efficiency change of health of health systems. The highest progress in technical efficiency change of 11.9% was registered in the year 2013 while the highest regress of 12.1% was registered in the year 2017. The findings in Table 8 also indicate a 0.9% regress in the technological change of health systems in African LDCs. The highest progress and regress of 18.4% and 11.4% of technological change were registered in the years 2017 and 2018 respectively. Furthermore, the average total factor productivity change for health systems in African LDCs in Table 8 was 1.003, representing a 0.3% increase in total factor productivity. The highest progress in total factor productivity change of 13.4% was in the year 2016 while the lowest of 0.1 was during the 2008-2010 period. The highest regress in total factor productivity change of 5.3% was in the year 2012 while the lowest regress in total factor productivity change was in the year 2014. The growth in total factor productivity over the years was largely from the technical efficiency change than the technical change. Malmquist index summary means of African LDCs for model 2 for the 2008-2018 period According to Kizza (2012) , group averages like the malmquist index summary of annual means for the best DEA Malmquist Model 2 in Table 8 hide individual results. As a result, it is crucial to run estimates for summary means for each African LDC for the 2008-2018 period (see Table 9 ). Results in Table 9 indicate a 1.2% progress in the technical efficiency of health systems for African LDCs over the 2008-2018 period. since technical efficiency = pure efficiency change × scale efficiency change, the mean technical efficiency change of 1.012 was as a result of 1.009 and 1.003 progress in pure efficiency change and scale efficiency change respectively. Table 9. Malmquist index summary means of (n = 29) African LDCs using model 2 for the 2008-2018 period. Country/DMU Technical efficiency change (effch) Technical change (techch) Pure efficiency change (pech) Scale efficiency change (sech) Total factor productivity change (tfpch) Angola 1.028 1.033 1.023 1.005 1.062 Benin 1.020 1.002 1.013 1.007 1.022 Burkina Faso 1 1.005 0.996 1.004 1.005 Burundi 1.038 1.038 1 1.038 1.077 Central African Republic 1.022 0.985 0.925 1.105 1.007 Chad 0.992 1.011 1 0.992 1.003 Democratic Republic of Congo 1 0.99 1 1 0.99 Djibouti 0.953 1.014 0.957 0.996 0.966 Eritrea 1.045 0.974 1.041 1.004 1.018 Ethiopia 0.994 0.981 0.995 0.999 0.975 Gambia 1.056 0.993 1.104 0.957 1.049 Guinea 1.064 0.914 1 1.064 0.972 Guinea-Bissau 1.073 0.905 1.077 0.997 0.972 Lesotho 0.962 1.033 1.003 0.959 0.993 Liberia 1.004 0.929 1.006 0.998 0.933 Madagascar 1.024 1.058 1 1.024 1.084 Malawi 1.027 0.987 1.103 0.931 1.013 Mali 1.05 0.975 1.053 0.997 1.024 Mauritania 1.001 1 1 1.001 1.001 Mozambique 1 0.958 1 1 0.958 Niger 1.034 1.027 1 1.034 1.062 Rwanda 1 0.998 1 1 0.998 Senegal 0.955 1.005 0.935 1.021 0.959 Sierra Leone 1 0.925 1 1 0.925 Sudan 0.886 1.035 0.944 0.939 0.917 Togo 0.96 0.984 0.944 1.017 0.944 Uganda 1.084 0.992 1.084 0.999 1.075 Tanzania 1.039 0.998 1.041 0.998 1.037 Zambia 1.055 1.016 1.037 1.017 1.071 mean 1.012 0.991 1.009 1.003 1.003 Seventeen (Angola: 2.8%, Benin: 2%, Burundi: 3.8%, Central African Republic: 2.2%, Eritrea: 4.5%, Gambia: 5.6%, Guinea: 6.4%, Guinea Bissau: 7.3%, Liberia: 0.4%, Madagascar: 2.4%, Malawi: 2.7%, Mauritania: 0.1%, Mali: 5%, Niger: 3.4%, Uganda: 8.4%, Tanzania: 3.9%, Zambia: 5.5%) African LDCs had progress in technical efficiency change meaning that they moved towards the frontier. Five (Burkina Faso, Democratic Republic of Congo, Mozambique, Rwanda and Sierra Leone) neither registered regress or progress in the technical efficiency change. Seven (Chad: 0.8%, Djibouti: 4.7%, Ethiopia: 0.6%, Lesotho: 3.8%, Senegal: 4.5%, Sudan: 11.4% and Togo: 4%) African LDCs registered regress in the technical efficiency change. These results are consistent with those of studies like Kim et al . (2016) and Hsu (2014) who also reported a progress in the technical efficiency change. However, they are in disagreement with those of Singh et al . (2021) and Kirigia et al . (2007) who reported a decline in the technical efficiency change. A possible explanation for this is the efficient use of resources in countries that demonstrated a progress and inefficient use of resources in countries that demonstrated regress. All African LDCs experienced a 0.9% mean reduction or regress in technology indicating that technical change for the African LDCs was less than one (<1). This meant that the technology (production) frontier shifted downwards. Sixteen African LDCs (Central African Republic, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea Bissau, Liberia, Malawi, Mali, Mozambique, Rwanda, Sierra Leone, Tanzania, Togo and Uganda) had a regress in technical change while twelve African LDCs (Angola, Benin, Burkina Faso, Burundi, Chad, Djibouti, Lesotho, Madagascar, Niger, Senegal, Sudan and Zambia) had progress or improvement in technical change over the 2008-2018 period. Mauritania is the only African LDC that had stagnation in technical Change. The regress in technical change experienced by the African LDCs during the 2008-2018 period is attributed to low adoption of technologies and to the use of outdated technologies. Similar results are reported by ( Hsu, 2014 ; Masri & Asbu, 2018 ; Singh et al ., 2021 ). Over period 2008 to 2018 period, there was a 0.3% progress in the total factor productivity change of health systems in African LDCs. This progress was due to 1.2% progress in technical efficiency change and 0.9% regress in technical change. Sixteen African LDCs (Angola = 6.2%, Benin = 2.2%, Burkina Faso = 0.5%, Burundi = 7.7%, Central African Republic = 0.7%, Chad = 0.3%, Eritrea = 1.8%, Gambia = 4.9%, Madagascar = 8.4%, Malawi = 1.3%, Mali = 2.4%, Mauritania = 0.1%, Niger = 6.2%, Uganda = 7.5%, Tanzania = 3.7% and Zambia = 7.1%) registered progress in the total factor productivity change of health systems in African LDCs. Thirteen African LDCs (Democratic Republic of Congo, Djibouti, Ethiopia, Guinea, Guinea Bissau, Lesotho, Liberia, Mozambique, Rwanda, Senegal, Sierra Leone, Sudan and Togo) registered regress in the total factor productivity change of health systems in African LDCs. These findings are in agreement with those of Ibrahim et al . (2019) , Kim et al . (2016) and Hsu (2014) and in disagreement with those of Kirigia et al . (2007) . A possible explanation for this according to Kim et al . (2016) and Cashin and Dossou (2021) are the several health policy reforms such as (easy access to primary care, better treatment procedures implementation of information technology and payment systems) amongst the African LDCs. Conclusion and policy recommendations Results of the total factor productivity growth of health systems in African LDCs from 2008 to 2018 indicated a 0.3% progress in the total factor productivity change. Sixteen African LDCs registered a decline in the total factor productivity growth while thirteen witnessed progress in the total factor productivity growth of their health systems. The variations in the total factor productivity growth of health systems of African LDCs are attributed to a 1.2% progress in technical efficiency change and a 0.9% regress in technical change. Less productive African LDCs are advised to bench mark the policies of productive African LDCs. Data availability Underlying data Data for each of 29 African LDCs on life expectancy at birth, maternal mortality ratio, under five mortality rate and infant mortality rate for the 2008-2018 period used in this study were sourced from the World Bank: https://databank.worldbank.org/source/world-development-indicators . The domestic general government health expenditure, out of pocket health expenditure, domestic private health expenditure and external health expenditure data used in this study were sourced from the World Health Organization health financing indicators section: https://www.who.int/data/gho/data/indicators/indicators-index . To access the data for each of the 29 African LDCs for the 2008-2018 period, each of the indicators is searched for from the list of indicators which are arranged in alphabetical order. Access to the source data is free of charge subject to the terms and conditions set by the World Bank ( https://data.worldbank.org/summary-terms-of-use ) and the World Health Organization ( https://www.who.int/about/policies/publishing/data-policy/terms-and-conditions ). The input and output data has been compiled and is provided on Zenodo below. Zenodo: Input and output data. https://doi.org/10.5281/zenodo.8007631 ( Musoke et al ., 2023 ). This project contains the following underlying data: - Input and Output Data.xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements We thank Makerere University, through its staff welfare and development that enabled the researcher to undertake this study. The entire team at the Makerere University school of economics that organized work in progress presentations and gave valuable contributions to the betterment of this work are also acknowledged. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 29 Aug 2023 ADD YOUR COMMENT Comment Author details Author details 1 College of Business and Management Sciences, Makerere University, Kampala, Uganda Edward Musoke Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Bruno Lule Yawe Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing John Ddumba Ssentamu Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 29 Aug 2023, 12:1050 https://doi.org/10.12688/f1000research.135418.1 Copyright © 2023 Musoke E 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 Musoke E, Lule Yawe B and Ddumba Ssentamu J. The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.12688/f1000research.135418.1 ) 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 1 VERSION 1 PUBLISHED 29 Aug 2023 Views 0 Cite How to cite this report: Arhin K. Reviewer Report For: The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.5256/f1000research.148534.r231745 ) The direct URL for this report is: https://f1000research.com/articles/12-1050/v1#referee-response-231745 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 25 May 2024 Kwadwo Arhin , Department of Economics, Ghana Institute of Management and Public Administration, Accra, Ghana Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.148534.r231745 This paper examines the productivity of healthcare resources in 29 least developed countries in Africa. Though the authors have made great efforts to contribute to the literature on health system efficiency and productivity, I think incorporating the following suggestions would ... Continue reading READ ALL This paper examines the productivity of healthcare resources in 29 least developed countries in Africa. Though the authors have made great efforts to contribute to the literature on health system efficiency and productivity, I think incorporating the following suggestions would go a long way to improve the quality of the paper. 1. One relevant recent literature on the productivity of health systems in Africa was left out in the literature review Arhin et al.,(2023) 1 2. How were the least developed countries (LDCs) defined? Is it based on the World Bank's definition for LDCs? What range of per capita GDP defines LDCs? The authors must clearly define LDCs. 3. To ensure that input variables used in the analysis are comparable across countries, the authors must use the variables measured in purchasing power parity (PPP) rate per capita instead of the current US$ per capita used in the analysis. The World Health Organization's Global Health Expenditure Database has all the input variables measured in PPP. 4. Out of Pocket Health Expenditure (OOP) is a subset of Domestic Private Health Expenditure (DPE) as correctly defined in Table 2. The addition of OOP to DPE as in Models 1 and 2 (see Table 5) adds little to no information to the data. The authors must consider including one of these two input variables in the estimation of models 1 and 2. 5. The policy recommendations section of the paper is a major weakness. There are no clear-cut policies emanating from the results and discussion of the paper. The authors must clearly explain at least three policy implications of the results of the paper and which agencies must implement those policy recommendations. Is the work clearly and accurately presented and does it cite the current literature? Partly 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? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly 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. Arhin K, Asante-Darko D: Performance evaluation of national healthcare systems in the prevention and treatment of non-communicable diseases in sub-Saharan Africa. PLoS One . 2023; 18 (11): e0294653 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health Economics, Healthcare Financing, Health Systems Efficiency and Productivity Analysis, and Health Econometrics. 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 Arhin K. Reviewer Report For: The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.5256/f1000research.148534.r231745 ) The direct URL for this report is: https://f1000research.com/articles/12-1050/v1#referee-response-231745 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 1 VERSION 1 PUBLISHED 29 Aug 2023 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 Version 1 29 Aug 23 read Kwadwo Arhin , Ghana Institute of Management and Public Administration, Accra, Ghana 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 © 2024 Arhin K. 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. 25 May 2024 | for Version 1 Kwadwo Arhin , Department of Economics, Ghana Institute of Management and Public Administration, Accra, Ghana 0 Views copyright © 2024 Arhin K. 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 This paper examines the productivity of healthcare resources in 29 least developed countries in Africa. Though the authors have made great efforts to contribute to the literature on health system efficiency and productivity, I think incorporating the following suggestions would go a long way to improve the quality of the paper. 1. One relevant recent literature on the productivity of health systems in Africa was left out in the literature review Arhin et al.,(2023) 1 2. How were the least developed countries (LDCs) defined? Is it based on the World Bank's definition for LDCs? What range of per capita GDP defines LDCs? The authors must clearly define LDCs. 3. To ensure that input variables used in the analysis are comparable across countries, the authors must use the variables measured in purchasing power parity (PPP) rate per capita instead of the current US$ per capita used in the analysis. The World Health Organization's Global Health Expenditure Database has all the input variables measured in PPP. 4. Out of Pocket Health Expenditure (OOP) is a subset of Domestic Private Health Expenditure (DPE) as correctly defined in Table 2. The addition of OOP to DPE as in Models 1 and 2 (see Table 5) adds little to no information to the data. The authors must consider including one of these two input variables in the estimation of models 1 and 2. 5. The policy recommendations section of the paper is a major weakness. There are no clear-cut policies emanating from the results and discussion of the paper. The authors must clearly explain at least three policy implications of the results of the paper and which agencies must implement those policy recommendations. Is the work clearly and accurately presented and does it cite the current literature? Partly 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? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly 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. Arhin K, Asante-Darko D: Performance evaluation of national healthcare systems in the prevention and treatment of non-communicable diseases in sub-Saharan Africa. PLoS One . 2023; 18 (11): e0294653 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Health Economics, Healthcare Financing, Health Systems Efficiency and Productivity Analysis, and Health Econometrics. 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) Arhin K. Peer Review Report For: The total factor productivity growth of health systems in African least developed countries [version 1; peer review: 1 not approved] . F1000Research 2023, 12 :1050 ( https://doi.org/10.5256/f1000research.148534.r231745) 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/12-1050/v1#referee-response-231745 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. 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