The Relationships between Temperature, Rainfall and the Incidence Rate of Malaria in South East Asia: A Panel Data Analysis

Transcription

1 The Relationships between Temperature, Rainfall and the Incidence Rate of Malaria in South East Asia: A Panel Data Analysis Karnjana Songwathana 1 ABSTRACT The malaria incidence varies greatly in South East Asia with high risk in Myammar and low risk in Malaysia and Thailand. This reflects the combined impacts of rapid changes in demographic, economics, social-economics and climate change. This study aims to analyze the impact of climate change and other factors on malaria incidence based on cross-country data from 7 Southeast Asia countries during Fixed effects and random effects specifications have been used to analyze this panel dataset. With hausmen test, the random effects specification is found to best capture the relationships between climate change and malaria incidence in cross-country level. The result shows that temperature has positive relationship with malaria incidence. Besides, higher per capita gross domestic product and higher population density can lead to lower malaria incidence. This finding is consistent with expectations that malaria risk considerably depends on temperature which may impact on vector control. Hence, the pattern of the malaria incidence will be affected by the projected change in temperature caused by climate change. A significantly decreased temperature will reduce the spread of malaria in Southeast Asia countries. The spread of malaria disease is likely to be reduced by higher economic development and urban area as represented by area with more densely populated. Keywords: malaria incidence, climate change, random effect specifications, panel data karnjana.s@bu.ac.th 1 Department of Economics, Bangkok University, karnjana.s@bu.ac.th

2 Introduction Malaria is a mosquito-borne infectious disease of humans and other animals caused by protozoan of the genus Plasmodium. An estimated 207 million cases of malaria and 627,000 deaths were reported in 2012 (WHO 2013). Although 90 percent of all malaria deaths occur in sub-saharan Africa, malaria is also counted as the most serious infectious disease in Asia. In 2011, approximately 1.33 billion people in Asia were at some risk of malaria, which was estimated about 75 percent of Asia s total population. 2,144, 849 malaria cases and 1,819 malaria deaths were reported in The high number of cases was a result from six most populous countries of Asia included India, China, Indonesia, Bangladesh, Vietnam and the Philippines (Bharati & Ganguly, 2013). Besides, 90 percent of reported malaria cases and deaths in the past 10 years were from India, Indonesia and Myanmar. In Southeast Asia region, the risk of malaria transmission varies greatly with low risk in Singapore and coastal area of peninsula Malaysia; however, on the Thailand borders with Cambodia, Laos and Myanmar including the Greater Mekong Subregion (GMS) which comprised of Cambodia, China's Yunnan Province, Lao PDR, Myanmar Thailand and Vietnam, are still at high risk. While global malaria cases and deaths have been greatly improved in the past decade, Southeast Asia region still has faced great challenges for malaria elimination especially in Myanmar and Cambodia. The malaria disease is complex and can be correlated with several factors, for example, vectors, human behaviors, socio-economics changes, environmental changes and climate changes that affect malaria transmission (Hongvivatana, 1986; Kamolratanakul, Dhanamun, & Thaithong 1992; Marten et al 1995; Roosihermiatie, Nishiyama & Nakae 2000) Although there have been several studies related to climate changes and malaria incidence (Goklany & King 2004; Tanser, Sharp & Sueur 2003), predicting the impact of climate changes on malaria incidence is complex and varied on different regions. This study aims to analyze the impact of climate change and other factors on malaria incidence based on cross-country data from 8 Southeast Asia countries during Conceptual Framework A conceptual framework for important factors influencing malaria incidence especially the number of case is illustrated in Figure 1, indicating that climate factor along with other factors are proposed to have significant impacts on malaria incidence. These other factors are grouped into variables that relate to malaria incidence based on key finding from previous studies, such as economic factors, demographic factors, and socioeconomics factors. Economic factors, and in particular, income is counted as an important factor for explain malaria incidence. It has been widely recognized that a

3 malarious community is an impoverished community (Weller, 1958). The reason why malaria incidence seems to be intractable for low income countries could relate to the affordability on the insecticide treated bed nets and well-health care treatment. Per capita gross domestic product is normally used as the income indicator or economic development across countries. In addition, income equality is usually associated with lower malaria cases. This could be from the result that greater income equality allows for improved anti-malaria and treatment access. Demographic factors especially, age in micro-level study and population size and population density in macro-level study are often reported as the factor determined malaria incidence. Fertility, migration and urbanization have substantially impact on malaria transmission. Low population densities in rural areas and high population densities in urban areas tend to influence malaria transmission (Tompkin & Ermert, 2013). In addition, socioeconomics factors in particular, education are also important factors determined malaria incidence. Education is usually proposed as the important factor that reduces malaria incidence. It is widely accepted that improving people s understanding of malaria disease is an important factor that affects people behavior in order to prevent malaria and consequently reduces malaria cases and deaths. The basic education tends to encourage people to know how to take care themselves in order to prevent malaria disease, for example, how to use the insecticide treated net properly, awareness of malaria symptoms, how to get treatment, and the important of getting treatment. Finally, climate factors especially global warming issue and precipitation rate are often analyzed as the determinants of malaria incidence. Climate change affects the range and abundance of species carrying diseases and pathogens (Bosello, Roson & Tol, 2006). The female mosquito is exponentially related to temperature, which implies that a slight increase in temperature can result in greater increase in the development rate of the parasite. In addition, increasing temperature also increase the rate that mosquitoes can digest people blood which implies that mosquitoes will be able to consumer more blood, bite and consequently infect more people (Finley-Jone, 2013). Apart from temperature, precipitation rate is also counted as a significant factor for malaria incidence. Rainfall is usually related to the survival of mosquito populations since mosquitoes tend to breed in the freshwater pools increased with the increasing rainfall. Hence, increasing rainfall will lead to higher number of mosquito population and consequently higher number of malaria incidence.

4 Economic Factors (Income, Income Inequality) Incidence Rate of Malaria Demographic Factors (Population Density) Socioeconomics Factors (Education) Temperature, Rainfall Figure 1 Conceptual Framework Empirical Model The main objective of this study is to analyze the relationship between climate factors and malaria incidence. In addition, this study aims to find out and analyze other important factors influenced on malaria incidence such as economic, demographic and socioeconomics factors. Hence, the conceptual framework leads to the empirical models. Besides, these regressions are analyzed using panel data. Based on the literature review and the conceptual framework, the empirical model postulates that malaria incidence, M it, is related to climate factor C it,, per capita income X it, (such as per capita GDP, GINI coefficient), a country s demographic factor D it, (such as population density), and socioeconomic factor, S it (such as enrollment rate). In addition, unobserved factor control variables are represented in term of time specific (τ) and country specific (ρ) effects while i = 1,., n province and t = 1,., T years. The relationships discussed above are summarized in the following general model. M it = f (C it, X it, D it,, S it :, ) (1) The estimation consists of the following steps. First, this study estimates each models from data pooled over time. Because each country has unique characteristics, it is important to capture unobserved country specific factors. Next, the study estimates fixed and random effects models. For

5 both fixed and random effects models, time dummies and time trend variables are sequentially added to the specification and tested. By examining, the relevant statistics, the study determines which specification is appropriate. This study also tested for different functional forms and determined whether log-log function provides the best fit. Data This study includes 8 countries in Southeast Asia region from , which are Indonesia, Myanmar, Thailand, Cambodia, Lao People's Democratic Republic, Malaysia, Vietnam and the Philippines. Malaria incidence rate which is the number of malaria cases per 10,000 people per year is calculated by the number of malaria case divided by the total population and multiplied by 10,000. The number of malaria cases was drawn from World Health Organization reports; while the total population counted all residents regardless of legal status or citizenship was from World Development Indicators report. Similar to the total population, per capita GDP based on purchasing power parity (PPP) which is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies gross converted to US dollars using purchasing power parity rates was from World Development Indicators. GINI coefficient measure the distribution of income among household within economy deviated from the perfect equal distribution. Basically, the range of GINI coefficient is between 0 and 1 or 0 percent and 100 percent, where 0 indicates perfect equality and 1 or 100 percent indicates maximum inequality. The data source was from World Development Indicators. Population density is midyear population divided by land area which includes country's total area excluding inland water bodies in square kilometers. The data sources were Food and Agriculture Organization and World Bank Population Estimates. In addition, gross enrollment ratio for tertiary which represents education factor is the percentage of total enrollment in tertiary education, regardless of age over the total population of the five-year age group. The data source was from UNESCO Institute of statistics. Climate factors which are temperature and precipitation data were draw from Climate Research Unit (CRU) at the University of East Anglia, and Mitchell, Carter, Jones, Huleme, & New (2003). In order to find the most appropriate model, all variables are logtransformed and tested for their relationships with malaria incidence rate. Results Descriptive statistics and correlation coefficients are found in Table 1 and Table 2, respectively. Results of the models linking malaria incidence rate climate factor, income factor, demographic factor, and socioeconomic factors are report in Table 3. These estimations do not include some variables that

6 the study collected, tested, but then excluded from the analysis for several reasons. Some variable such as the number of hospital beds are highly correlated with variables included in the analysis. In addition, Singapore and Brunei which are also in Southeast Asia region are excluded from the sample since malaria is not susceptible in those countries. From Table 1, the average of malaria incidence rate is approximately 21 cases per 10,000 people with the highest rate about 89 cases per 10,000 in Myanmar. The range of temperature is from to Celsius with the average of temperature at Celsius; meanwhile the range of precipitation rate is from 1, to 3,459 millimeter with the average of precipitation at 2, millimeters. Per Capita GDP and Population Density are quite varied. Malaysia has the highest per capita GDP; while the Philippines have the highest population density among Southeast Asia countries. Table 1 Descriptive Statistics Variable Unit Mean Std. Dev. Min Max Malaria Incidence Rate per 10,000 people Temperature Celsius Rainfall Millimeters 2, , , Population Density % of total Per Capita GDP US$ 4, , , , Gross School Enrollment in Tertiary % of total GINI coefficient % Table 2 Correlation between Explanatory Variables Temperature 1.00 Rainfall Population Density Per Capita GDP Gross School Enrollment in Tertiary GINI coefficient

7 Table 2 represents the correlation between explanatory variables in the empirical model. None of explanatory variable has the absolute value of correlation over 0.80; therefore, this empirical model has no multicollinearity problem. Table 3 Regression Results when All Variables Are Log-transformed Pooled-OLS Fixed Effects Random Effects Std. Ln Malaria Incidence Rate Coef. Err. Coef. Std. Err. Coef. Std. Err. Ln Temperature * * 2.26 Ln Rainfall Ln Population Density * * 0.19 Ln GDP per Capita * *** * 0.40 Ln School Enrollment Ln Gini Constant *** *** ** 8.36 F-test 5.15** Hausman Test 4.41 *indicates significant at 1% level and ** indicates significant at 5% level *** indicates significant at 10% level In order to find the relationships between each factor and malaria incidence rate, this study compares pooled-ols, fixed effects and random effects and chooses the best specification. Table 3 represents the result when all variables are log-transformed. According to significance of F-test from testing specific country effect, the estimated coefficient from pooled-ols specification is insufficient. Hence, the study compares two main approaches: fixed effects and random effects. Hausman test shows that the random effects specification is the most appropriate specification for this case. Unlike the fixed effects specification, the rationale behind random effects specification is that the variable across entities is assumed to be random and uncorrelated with malaria incidence rate or other explanatory variables in the model. This study assumes that that the differences across the countries have some influence on malaria incidence; therefore, random effects specification is the best fit. Besides, random effect specification allows the model to include rarely changing variable such as temperature data in the tropical region.

8 When the most appropriate specification is considered, the sign of coefficient of per capita GDP is negative and statistically significant. The finding confirms that the reduction in malaria incidence rate can be attained through increases in country s income. In addition, population density also shows significantly negative sign, which could be explained that higher population density unexpectedly can reduce malaria incidence rate. This could be because higher population density area in Southeast Asia tends to be urban area or cities with low risk. School enrollment and income equality has no significant impact on malaria incidence rate. In term of climate indicators, temperature shows statistically significant positive sign. The finding confirms that higher temperature leads to higher malaria incidence rate. Although, precipitation unexpectedly shows positive sign, there is no statistically significant. These results help identify and explain the relationships between major factors and malaria incidence rate in Southeast Asia region. These results also confirm that malaria incidence rate in Southeast Asia region is associated with economic, demographic and climate change that Southeast Asia has undergone over the past years. In order to manage malaria control strategies in Southeast Asia, policy makers should consider not only one perspective but also economic, demographic, socio-economic and climate factor especially on temperature. According to the results, temperature could have remarkable impact on malaria incidence rate which policy makers could develop strategies and policies to control in order to reduce the health impact from malaria that may occur. In addition, the basic problem when analyzing the determinants of malaria incidence rate at the national level is the lack of micro or household data such as household behavior or climate change in term of disaster. This study could show the fundamental relationships which could lead to micro or household level study in the future. Conclusion This study explores the complex relationships between malaria incidence rate, economic, demographic, socio-economics and climate factors in Southeast Asia region. This study takes a step in that direction by compiling a panel dataset and careful testing to identify the most appropriate econometric model, including fixed and random effects specification. As shown by the empirical evidence, per capita GDP and population density have negative significant impact on malaria incidence rate. In term of climate factors, the results show that only temperature has significant positive impact on malaria incidence rate. This is signal that policy makers should concern on controlling temperature or global warming issue that could have reduce risk from malaria disease.

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