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A number of studies exist on the relationship between climatic factors and malaria prevalence. However, due to scarcity of data, most of the studies are based on biophysical experiments and do not control for socioeconomic covariates. This research, which uses data on Ghana, contributes to the thin literature that addresses this limitation. We found that humidity and rainfall predict malaria prevalence. Furthermore, our results suggest that malaria prevalence increases with rainfall, the proportion of middle income households, and the proportion of households with no formal education. The corresponding elasticity coefficients are 0.67, 0.12 and 0.66, respectively. Significant differences in the prevalence rate have also been observed across regions.
Although estimated global incidence of malaria fell by 17% between 2000 and 2010, the number of cases remains very high [
In addition to the morbidity estimates, the economic cost of malaria is staggering. In 2008, the costs of foregone production alone in Africa totaled about US$ 12 billion [
Global climate change or variability, and extreme climate events have been noted to partially explain malaria prevalence [
However, outside of the laboratory, the relationship between malaria transmissibility and temperature and precipitation is generally complex due to nonlinear impacts on mosquito population by climatic factors, as well as the interactions among such variables. For example, Thompson
To address these limitations, a few attempts have been made at investigating the impact of climate and economic variables on malaria incidence in Africa and elsewhere. In a recent study, EgbendeweMondzozo
This study contributes to the malariaclimate variability nexus by investigating whether or not climatic factors predict malaria incidence in Ghana. It also establishes a link between a number of socioeconomic variables and malaria prevalence using the limited available data on the relevant variables. We have found that at the district level, humidity and total rainy days in a year appear to be predictors of malaria prevalence within the country. A univariate time series analysis on the series reveals the malaria prevalence is rising over time. Furthermore, a crosssectional analysis at the district level indicates that increased number of rainfalls increases malaria prevalence, but providing formal education equivalent to the percentage increase in the number of rainfalls could mitigate the impact of the rainfall. It is also important to note that, in contrast to expectation, districts with a higher proportion of middle income households, on average, had higher malaria prevalence.
The remainder of the paper is organized as follows.
Ghana has three malaria epidemiologic zones: the northern savannah, the tropical rainforest, and the coastal savannah/mangrove swamps. The country’s entire population of 25.2 million people is spread across the three zones, and, subsequently, is at risk of getting malaria. Two dominant malaria vector species are found in Ghana:
The disease is a major cause of morbidity and mortality in the country and the pattern has been stationary over the years. It is estimated that each year about 3.5 million Ghanaians get malaria [
Infection rates are high in children and pregnant women. In Ghana, about 20,000 children die from the disease each year [
Malaria in Ghana is estimated to cause a loss of 10.6% DALYs, which translates into an economic impact equivalent to 6% of the annual GDP. At the level of the individual household, a malariastricken family, in addition to paying prevention costs and suffering loss of income, spends an average of over 25% of its income on malaria treatment. Such families harvest 40% of the crops harvested by healthy families. In areas where the disease is endemic, up to 60% of children’s schooling may be impaired as a result of repeated bouts of malaria [
Since 2003, the government of Ghana, in collaboration with a number of donor agencies, has embarked on numerous initiatives to combat malaria. The first initiative, which began in 2003, was called Roll Back Malaria (RBM) and was designed to strengthen health services and make effective prevention and treatment strategies more widely available [
Another Ghanaian initiative is the Intermittent Preventive Treatment (IPT) for pregnant women. In this treatment program, pregnant women in their second and third trimesters are administered at least two doses of the drug sulfadoxinepyrimethamine (SP) at least one month apart. This significantly reduces the proportion of lowbirth weight infants and reduces maternal morbidity, and has been piloted and promoted in the upper East, upper West and Northern regions of Ghana [
Currently, an ArtesunateAmodiaquine combination, which belongs to Artemisininbased Combination Therapies (ACTs), has been selected as the first line drug for the treatment of uncomplicated malaria in Ghana. The criteria for selecting these combinations include its efficacy, compliance, side effects, cost effectiveness, and appropriateness for treating malaria in children and in pregnancy. A recent test has found that ArtesunateAmodiaquine combination has a clinical response of 97% [
In spite of all the policies and programs, there is little evidence that Ghana has managed to greatly reduce malaria prevalence over the years [
A number of empirical models have been used in our analysis. First, to investigate whether the climate variables predict malaria incidence in each of the districts as well as the aggregate of the districts, the Augmented DickeyFuller (unit root) and Granger causality tests were employed. Furthermore, due to the limited crosssectional data points, Ordinary Least Square (OLS) regression analysis and Generalized Maximum Entropy (GME) method, which is a semiparametric method, were employed to investigate the determinants of malaria incidence in Ghana in 2008.
Granger causality analysis, which is based on multiple regression analysis, is a method employed to investigate whether a time series can correctly forecast or predict another [
The empirical equation to be estimated is:
It is very likely that some of the variables at the right hand side of Equation 3 are highly correlated. In addition, the limited number of observations may result in biased and inconsistent estimates. As a result, the coefficients are estimated using the GME method, which could generate reliable estimates of the parameters of our model. The GME is a semiparametric estimator and belongs to a class of estimators used in engineering and physics. To present the GME estimator, let
The Ministry of Health’s districtlevel data on malaria was used for the analysis. The time series consists of 48 observations (January 2008 to December 2011). The climate variables of interest include rainfall, temperature, humidity and wind speed. For the rainfall series, the number of rainy days was used instead of volume of rain, since the former generated better results. The 2008 climate data collected from the Ghana Meteorological Agency is limited to the 22 meteorological/weather stations [
The results of the Augmented DickeyFuller tests for unit root are presented in
Unit Root Test on Malaria Incidence and Climate Variables (Ghana: January 2008–December 2011).
Variable  Lags  

Malaria Incidence  −3.860  0.0137**  3 
Humidity  −4.719  0.0006***  3 
Temperature  −3.965  0.0098***  3 
Rainy Days  −3.815  0.0158**  3 
Wind speed  −3.434  0.0470**  3 
Note: ** significant at 5%; *** significant at 1%.
The descriptive statistics of the malaria incidence and its potential determinants in Ghana in 2008 is given in
Descriptive Statistics of Malaria Prevalence and its Determinants (2008).
Variables  # of Districts  Mean  Standard Deviation 

Malaria Prevalence (Monthly)^{1}  19  0.021  0.0082 
Rains (# of Yearly Rainy Days)  20  104.050  19.7230 
No Formal Education (Proportion)  20  0.722  0.2139 
Middle Income (Proportion)  20  0.235  0.1967 
Eastern Region (1/0)^{2}  20  0.150  0.3663 
Note: 1. The malaria prevalence is measured as the proportion of the population who had malaria with a month. 2. Eastern region is a dummy variable. Thus, the variable takes the value “1” if the data pertains to Eastern region and “0” otherwise.
First, after performing unit root tests (see
Next, attempts have been made to forecast malaria prevalence and total rainy days. The BoxJenkings approach to univariate time series econometric modeling was employed. The plots of the autocorrelation and partial autocorrelation functions of the series revealed the malaria prevalence series follow an Autoregressive Moving Average (ARMA) process. A further analysis reveals that the variable could be modeled as ARMA (2, 1) process (
Granger Causality Test on Malaria Incidence and Climate Variables (Ghana: January 2008–December 2011).
VARIABLE  RRSS  URSS  Fstats 

Humidity  0.000445741  0.000384248  3.281** 
Temperature  0.000445741  0.000419099  1.303 
Rainy Days  0.000445741  0.000312815  8.711*** 
Wind speed  0.000445741  0.000398086  2.454 
Note: 1. ** significant at 5%; *** significant at 1%. 2. RRSS is Restricted Residual Sum of Squares, and URSS stands for Unrestricted Residual Sum of Squares.
ARIMA Regression: Incidence of Malaria in Ghana (January 2008–December 2011).
Varibale  Coefficient  Standard Error 

Auto Rregresive (AR)  
Lag 2 (L2).  0.567  (0.1707)*** 
Moving Average (MA)  
Lag 1 (L1)  0.892  (0.0994)*** 
Constant  0.029  (0.0020)*** 
Wald chi2(2) = 82.90: Prob > chi2 = 0.0000  
# of obs = 48 
Note. 1. *** significant at 1%. Standard errors in parentheses. 2. ARIMA is Autoregressive Integrated Moving Average.
The Actual and Fitted Values of Malaria Prevalence in Ghana (January 2008–December 2011).
The univariate analysis of total rainy days is reported in
ARIMA Regression: Total Rainy Days in Ghana (January 2008–December 2011).
Varibale  Coefficient  Standard Error 

Auto Rregresive (AR)  
Lag 1 ( L1)  0.520  (0.12219)** 
Moving Average (MA)  
Lag 1 (L1)  0.301  (0.2583) 
Constant  97.149  (18.7753)*** 
Wald chi2(2) = 22.62: Prob > chi2 = 0.0000  
# of Obs = 48 
Note: 1. ** significant at 5%; *** significant at 1%. Standard errors in parentheses. 2. 2. ARIMA is Autoregressive Integrated Moving Average.
The Actual and Fitted Values of Total Rainy Days for 22 Districts in Ghana (January 2008–December 2011).
As noted earlier, Ghana’s 2008 climate data, malaria prevalence data, and data on some socioeconomic variables were combined to explore determinants of districtlevel malaria prevalence. Since climate data exists for 22 weather stations—each situated in an administrative district—only those were considered for the analysis. The small number of observations limits the number of potential explanatory variables that could be explored. To improve the robustness of the parameter estimates, a simple OLS was complemented with bootstrap estimates. In addition, as indicated earlier, the GME estimation method was employed to further verify the robustness of the coefficients. The (pseudo) Rsquared was used to verify the goodness of fit for the three results.
The results of the simple OLS and the bootstrap estimates, with 20 replications, were strikingly similar. For both estimations, the Rsquared indicates that about 65% of the variability of malaria incidence is explained by explanatory variables (rains, income, education, and regional differences). The corresponding pseudo Rsquared for the GME is slightly lower (61%). The results from the OLS reveals the “number of rainy days” and “proportion of middle income households within a district” are significant at 5% levels, and each of the other two variables (proportion of households without formal education and households in the Eastern region) was significant at the 1% level.
Regarding the coefficients of the specific variables, the elasticities are striking. The variable with the highest coefficient is the number of rainy days. On average, a 1% increase in the mean number of rainy days, all else equal, increases the incidence by 0.67%. Put differently, a district that has, say, 1% more average rainy days than the other districts experiences a 0.7% higher malaria prevalence, all else being equal. The 1% change in the mean prevalence is equivalent to 4,620 individual cases a month. With the average estimated economic cost of GH¢30.04 to GH¢32.65 per person, this will total GH¢138,784.80 to GH¢150,843.00 for the entire country per month (GH¢1.00 = US$0.52). Since outpatient visits amount to only 46% of the prevalence, the overall cost could be at least twice as high as these estimates. On the other hand, it has been found that the use of insecticidetreated mosquito nets can reduce transmission by a quarter (25%), and households on the average spend GH¢2.48 (US$1.30) on mitigation products (aerosol sprays, mosquito coils and bed nets) [
Secondly, a district with a 1% higher proportion of households with no formal education, on average, has a 0.66% higher malaria incidence. Providing formal education may create awareness and improve the socioeconomic conditions of households. This could invariably impact malaria prevalence in Ghana. The elasticity coefficients of rainy days and households without formal education are approximately the same, implying that provision of education could potentially mitigate the climate factor (number of rainy days) impact on malaria.
Perhaps, the most striking result is the relationship between income and malaria prevalence. Districts with a higher proportion of middle income households, on average, also had a higher incidence of malaria. The corresponding elasticity coefficient is 0.12. The possible explanation is that since the data is collected at health posts, the poor may not be adequately captured since they may rely more on herbal medication or selfmedicate. It is also perhaps not farfetched to speculate that the poor may have better immune systems and for that matter do not visit health facilities frequently. On the other extreme, the rich are able to afford protection against the disease.
Finally, compared to the other nine regions in the country, the Eastern region recorded the highest malaria incidence, though the difference between it and the other regions’ incidence levels was marginal. From the coefficient, households in the region on average have a 0.16% higher prevalence than their counterparts in the other regions. Consequently, regional differences must be considered when designing public policy on malaria mitigation.
Ordinary Least Square (OLS) and Generalized Maximum Entropy (GME) Estimation of the Impact of Rainfall on Malaria Incidence in Ghana.
Variable  Regression 1

Regression 2

Generalized Maximum Entropy (GME) Estimates  

Coefficient  Elasticity  Coefficient  Elasticity  Coefficient  Elasticity  
Rains (# of rainy days)  0.000136 (0.000065)**  0.670  0.000136

0.670  0.00022

1.113 
No Formal Education (Proportion)  0.019026

0.661  0.019026

0.661  0.01999

0.702 
Middle Income (Proportion)  0.010984

0.124  0.010984

0.124  0.01015

0.116 
Eastern Region (1/0)  0.012654

0.065  0.012654

0.065  −0.9909


Constant  −0.010702

−0.010702

−0.01934


RSquared  0.65  0.65  0.61 
Note: *** significant at 1%;** significant at 5%; * significant at 10%. Standard errors in parentheses.
Malaria remains a major communicable disease in most subSahara African countries, imposing heavy fiscal burdens on households and governments. Ghana typifies the situation on the subcontinent. The effectiveness of public health policy in addressing the problem depends on how well the drivers or covariates are identified. Existing studies, which are largely experimental, have found a correlation between malaria incidence and climate variables (e.g., temperature). Using time series data, our study found that, at the national level, the total number of rainy days and humidity predict malaria incidence.
Further, our crosssectional analysis, with districts as the units of analysis, reveals that in addition to total rainy days, formal education, income levels, and regional differences account for variations in malaria incidence. The striking findings show that an equal percentage increase in formal education could mitigate the higher malaria incidence caused by an increase in rainy days. As a result, public policy must be directed at increasing literacy rates within the country. Perhaps, more surprising is the finding that middle income earners are more likely to suffer from the disease. It follows that, to reduce the prevalence of the disease, subsidized mosquito nets and insecticides should not only be made available to the poor, but to middle income households as well.
Notwithstanding the contribution of this study to public policy on malaria control in Ghana, the research has some shortcomings, especially with regards to the limited data used. Thus, the limited number of districts with weather stations and the single year’s worth of socioeconomic data restricted the number of variables to be included in the regression analysis. Future research on the topic should consider using panel data or repeat the crosssectional analysis with much more localized climate data when available. Disaggregated analysis may also generate better results, since ecological transmissions are observed at microlevels within districts.
The authors would like to express their profound gratitude to two anonymous referees of the journal for their invaluable comments. In addition, we are very grateful to Anatu Mohammed for her invaluable comments, and CEERAC research assistant Isaac K. Ampiaw for assisting with the data compilation. Financial support from the International Development Research Centre (IDRC), Canada, for the African Adaptation Research Centre (AARC) initiative, which is a part of IDRC’s Climate Change Adaptation Research and Training Capacity for Development (CCARTCD) Program, is highly appreciated.
The authors declare no conflict of interest.
The twenty weather stations are located at Abetifi, Accra, Ada, Akatsi, AkimOda, Axim, Akuse, Bole, Ho, KeteKrachi, Koforidua, Kumasi, Navrongo, SefwiBekwai, Saltpond, Sunyani, Takoradi, Tamale, Tema, Wa, Wenchi, and Yendi. As of 2008, Ghana had 170 districts
Unit Root Test of Malaria Incidence and Climate Variables (Humidity, Temperature, Rainy Days, Wind Speed).
Variable  District  lags  


Abetifi  −3.521  0.0372**  0 
Accra  −16.414  0.0000***  3  
Ada  −7.277  0.0000***  3  
Akatsi  −3.820  0.0156**  2  
Akuse  −3.766  0.0184**  1  
Axim  −3.231  0.0784*  0  
Bole  −3.444  0.0458**  0  
Ho  −7.754  0.0000***  3  
Koforidua  −3.383  0.0537*  3  
Krachi  −6.412  0.0000***  3  
Kumasi  −3.903  0.0120**  3  
Navarongo  did not pass  
Oda  −15.590  0.0000***  3  
Saltpond  −3.609  0.0291**  3  
Sbekwai  −3.223  0.0799***  2  
Sunyani  −3.267  0.0719***  3  
Takoradi  −15.943  0.0000***  3  
Tamale  −3.964  0.0099***  3  
Tema  −6.985  0.0000***  3  
Wa  −3.244  0.0759*  3  
Wenchi  −4.991  0.0002***  3  
Yendi  −4.176  0.0049***  3  

Abetifi  −3.834  0.0149**  3 
Accra  −4.347  0.0027***  3  
Ada  −3.390  0.0528*  1  
Akatsi  −4.366  0.0025***  3  
Akuse  −4.123  0.0058***  3  
Axim  −3.738  0.0200**  3  
Bole  −5.400  0.0000***  3  
Ho  −4.997  0.0002***  3  
Koforidua  −4.607  0.0010***  3  
Krachi  −4.670  0.0008***  3  
Kumasi  −4.082  0.0067***  3  
Navarongo  −6.275  0.0000***  3  
Oda  −9.309  0.0000***  3  
Saltpond  −4.742  0.0006***  3  
Sbekwai  −6.224  0.0000***  3  
Sunyani  −4.241  0.0039***  3  
Takoradi  −7.003  0.0000***  3  
Tamale  −4.457  0.0018***  3  
Tema  −7.475  0.0000***  3  
Wa  −5.731  0.0000***  3  
Wenchi  −3.961  0.0100***  3  
Yendi  −4.299  0.0032***  3  

Abetifi  −5.437  0.0000***  3 
Accra  −5.147  0.0001***  3  
Ada  −4.025  0.0081***  3  
Akatsi  −5.353  0.0000***  3  
Akuse  −4.373  0.0024***  3  
Axim  −4.508  0.0015***  3  
Bole  −3.603  0.0296**  3  
Ho  −6.742  0.0000***  3  
Koforidua  −3.580  0.0316**  3  
Krachi  −3.984  0.0093***  3  
Kumasi  −4.871  0.0004***  3  
Navarongo  −3.952  0.0103**  3  
Oda  −3.725  0.0208**  3  
Saltpond  −4.414  0.0021***  3  
Sbekwai  −9.670  0.0000***  3  
Sunyani  −5.998  0.0000***  3  
Takoradi  −6.032  0.0000***  3  
Tamale  −4.366  0.0025***  3  
Tema  −3.921  0.0113**  3  
Wa  −4.195  0.0046***  3  
Wenchi  −4.928  0.0003***  3  
Yendi  −5.052  0.0002***  3  

Abetifi  −3.580  0.0316**  3 
Accra  −4.349  0.0026***  3  
Ada  −3.560  0.0334**  3  
Akatsi  −4.403  0.0022***  3  
Akuse  −4.447  0.0018***  3  
Axim  −3.503  0.0391**  3  
Bole  −3.653  0.0256**  3  
Ho  −3.583  0.0313**  3  
Koforidua  −4.399  0.0022***  3  
Krachi  −5.168  0.0001***  3  
Kumasi  −3.444  0.0458**  3  
Navarongo  −4.793  0.0005***  3  
Oda  −3.581  0.0315**  3  
Saltpond  −4.900  0.0003***  3  
Sbekwai  −3.643  0.0264**  3  
Sunyani  −3.437  0.0466**  3  
Takoradi  −3.910  0.0117**  3  
Tamale  −4.517  0.0014***  3  
Tema  −3.717  0.0213**  3  
Wa  −3.962  0.0099***  3  
Wenchi  −4.376  0.0024***  3  
Yendi  −5.171  0.0001***  3  

Abetifi  −3.352  0.0581*  3 
Accra  −4.396  0.0022***  3  
Ada  −4.009  0.0085***  3  
Akatsi  −4.019  0.0083***  3  
Akuse  −4.059  0.0072***  3  
Axim  −3.771  0.0181**  3  
Bole  −4.813  0.0004***  3  
Ho  no observations  
Koforidua  −3.504  0.0390***  3  
Krachi  did not pass  
Kumasi  −5.139  0.0001***  3  
Navarongo  did not pass  
Oda  −9.022  0.0000***  3  
Saltpond  −3.906  0.0119**  3  
Sbekwai  −4.504  0.0015***  3  
Sunyani  −5.391  0.0000***  3  
Takoradi  no observations  
Tamale  −4.670  0.0008***  3  
Tema  −3.770  0.0181**  3  
Wa  no observations  
Wenchi  −3.801  0.0165**  3  
Yendi  −3.617  0.0285**  3 
Note: *** significant at 1%;** significant at 5%; * significant at 10%.
Unidirectional Causality between Malaria Prevalence and Climate Variables in Ghana (January 2008–December 2011).
District  Humidity  Temperature  Rainy Days  Wind speed 

Abetifi  No  No  No  No 
Accra  No  Yes (***)  Yes (***)  No 
Ada  Yes(**)  Yes (***)  No  Yes (***) 
Akatsi  No  No  Yes(**)  No 
Akuse  Yes (***)  Yes(**)  Yes (***)  No 
Axim  Yes (***)  No  Yes (***)  No 
Bole  No  No  No  No 
Ho  Yes (***)  No  Yes(**)  No 
Koforidua  Yes (***)  No  No  No 
Krachi  Yes (***)  No  No  No 
Kumasi  No  No  No  No 
Navarongo  No  No  Yes (***)  No 
Oda  No  No  No  No 
Saltpond  No  Yes (***)  No  No 
Sbekwai  No  No  No  No 
Sunyani  No  No  No  No 
Takoradi  Yes (***)  No  No  No 
Tamale  Yes (***)  Yes (***)  Yes (***)  Yes (***) 
Tema  No  Yes (***)  No  No 
Wa  Yes (***)  No  Yes (***)  No 
Wenchi  Yes (***)  No  No  Yes (***) 
Yendi  Yes (***)  Yes (***)  Yes (***)  Yes (**) 
Note: *** significant at 1%;** significant at 5%; * significant at 10%.