Malaria is a major cause of ailment, resulting in approximately 243 million incidents of clinical malaria and claiming 863 thousand lives (World malaria report, 2009).Most of these cases occur in Sub-Saharan Africa and prevalence is high among children(WHO,2010). The World Health Organisation (WHO) advocates the use of indoor residual spraying (IRS) or insecticidal treated nets (ITNs) for vector control, immediate diagnosis and treatment of clinical malaria and Intermittent Preventive Treatment in pregnancy (IPTp) for pregnant women in high malaria area. The treatment involves administration of at least 2 dosages of sulphadoxine-pyrimethamine (SP) during the last two trimesters of pregnancy((WHO, 2010a)
Plasmodium parasites give rise to Malaria. Infected Anopheles mosquitoes are the vectors that disperse these parasites by means of bites, especially during the night. The severity of spread depends on aspects associated to the vector, the parasite, the human host, and the environment. Plasmodium falciparum, vivax, malariae and ovale are the common types of parasites occurring in humans. Out of these Plasmodium falciparum and vivax are the most common and plasmodium falciparum is the most fatal (WHO, 2014).
Malaria is common in Sub-Saharan Africa because of poverty and climatic conditions which are favourable for both the anopheles mosquito and the malarial parasites to multiply (Sachs&Malaney, 2002).
The United Republic of Tanzania includes both the Mainland and Zanzibar and has a population of 37.4 million (WHO, 2012) of which 90% of the population is at risk of getting malaria as mapped by Mapping Malaria Risk in Africa (MARA) (Le Sueur et al, 1998) The country is on number three after Nigeria and the Democratic Republic of Congo in terms of risk of stable malaria (5MARA-lite software).
Malaria threatens the health and financial wellbeing of Tanzania with 120,000 deaths annually of which 70,000 are children under five years .The yearly incidence rate is 400’500/1,000 people and is twice as much for children below five years(Ministry of Health,2003). Malaria accounts for the loss of productivity in 15-56 age groups and is a hindrance to the learning capacity of people between 5 ‘ 25 years of age (WHO, 2002) .It also deters foreign investment.
Tanzania is among impoverished countries with a GDP of 280 USD (2004) and 36% of the population living below the poverty datum line (National Bureau of Statistics, 2003). Malaria accounts for 3.4% of the GDP annually and every $2.14 of $11 budgeted for every person for health per annum is spent on malaria(Ministry of finance Tanzania,2001).The expenditure is proportioned to 75%, 20% and 5% by households, government and development partners respectively(Jewett et al,2000). At household level, 30 percent of the expenses are used for anti-malaria drugs and 50% for mosquito nets, insecticides, coils, and other prevention measures (Ministry of finance Tanzania, 2001).
MULTILEVEL FRAMEWORK APPROACH
It promotes understanding of how health determinants at different levels operate and how they are interrelated. These levels are at either individual, household, community, national/provincial or international .It also helps to understand the factors underlying the impact or success of policies and programmes designed to address these problems((WHO, 2010b) .This knowledge can be applied to shape and inform health policies and interventions at different levels.Overally,it helps individuals to understand that health is not merely a medical issue but there are more social issues involved.
INDIVIDUAL BIOLOGICAL DETERMINANTS
The younger you are the more vulnerable you are to malaria. This has been shown in children less than five years of age in several studies done in different countries with high prevalence of malaria.48 independent studies revealed that malaria is the main cause of death in children less than five years in Africa including Tanzania. They deduced that the occurrence of parasites among children was more than twice the cause of mortalities((Breman et al., 2004)
In the period 1982 to 1989 deaths in children under five years old rose from 31 to 55 per 1,000 with a parasite prevalence range of 18% to 95%.From 1990 to 1999, mortality cases in children below five years grew from 8 per 1000 to 44 per 1,000 over an array of prevalence of 0’95 %.( Snow et al., 2004). Van Geertruyden and others reviewed 117 studies which showed that the perinatal mortality rate (PMR) was 61.1 per 1,000 infants and 25.8 per 1,000 infants in malaria prone and non-malaria prone nations respectively(van Geertruyden et al.,2004).Likewise, the foetal mortality rate was more pronounced in malaria-prevalent countries.
The literature on the occurrence of malaria among males and female is inconsistent and it varies according to where you get the information, either from the health facility or from the community. For instance, a study in Thailand established that the male to female proportion of malaria incidence was 6:0 in a clinic and 1:0 in the community (Vlassoff&Bonilla, 1994). Overally, sex seems not to be directly linked to malaria susceptibility apart from pregnancy, so male and females are equally susceptible to malaria.
In malaria afflicted zones, expectant mothers have reduced resistance to malaria especially during the first 2 pregnancies. This is attributed to increased clinical episodes, pregnancy related anaemia, morbidity and death (WHO, 2002). The sequestration of parasites in the placenta results in babies being underweight at birth, undesirable effects on lactation, higher incidents of miscarriage and stillbirth (Sharp&Harvey, 1980).Pregnant women are more vulnerable to malaria than non-pregnant women.
The extent to which resistance to malaria is attained by individuals living in malaria endemic areas varies according to the level of exposure and genetically determined immune response (Trape & Rogier, 1996). In regions of high constant spread of malaria, the frequency of clinical malaria reaches its pinnacle during the first five years of age, and then reduces drastically as efficient immune reactions develop(Sachs&Malaney,2002;Trape&Rogier,1996) Where malaria prevalence is less , the peak age occurs later in childhood . In low-infection or malaria prone areas, susceptibility to malaria remains the same in all ages because protective immunity is never acquired (Sharp&Hervey, 1980; Kleinschmidt&Sharp; 2001). Resistance is not permanent and is lost when there is no repeated exposure to infections.
Co-infection with HIV increases the degree and severity of malaria infection. Despite initial studies suggesting no association between malaria and HIV infection, there is emerging evidence of an important relation, particularly in pregnant women. HIV infection may interfere with pregnancy-specific immunity acquired during first and second pregnancies and increases the chance of parasitaemia and placental malaria (Skeketee, 1996; Verhoeff, 1999). There is also a growing body of evidence that non-pregnant HIV-positive individuals are more vulnerable to malaria infection and to severe disease than those without HIV infection and that this susceptibility is related to the degree of immunosuppression (French, 2001; Grimwade, 2003; Shaffer, 1990; Whitworth; 2000).
Genetic makeup has an influence on the level of immunity an individual develops to counter malaria infection. The Fulani ethnic group for example, has less parasitaemia and malaria ailment and more malaria antibody titres than other equally vulnerable groups living in the same region (Modiano et al,; 1999; Riley et al,; 1992). In malaria- endemic areas, there is a high occurrence of genes that cause red-cell irregularities like sickle-cell disease and glucose-6-phosphate dehydrogenase deficiency. However they offer a selective benefit of protection against malaria mortality (Aidoo et al, 2002). HLA B53 and MHC antigens have been associated with defence against severe illness and reduced vulnerability to malaria fever respectively (Hill et al,; 1991).
INDIVIDUAL SOCIAL DETERMINANTS
Health seeking behaviour
Factors such as culture norms, society, literacy level, educational attainment shape a person’s health seeking habits. In the case of the Bondei people who are situated in north-eastern part of Tanzania, mothers and cohabiting relatives are often the first to observe a possible illness in children. They also determine whether the ailment warrants medical or traditional treatment. Fathers as the sponsors have the final say in determining the treatment choice. (Oberlander and Elverdan, 2000). This shows how health seeking behaviour can be influenced by the people around you and culture. Women in general tend to visit the hospital more often; the only problem is when they don’t have the means in terms of finance or approval from the husbands. Men tend to seek treatment when they are in a critical stage, mainly because of their ego. Pregnant women and children are given first preference and they tend to go the hospitals more often.
Education versus Knowledge
If someone is educated it doesn’t mean he or she is knowledgeable. Education is attained and knowledge is acquired. Someone might have tertiary education but might not have knowledge on malarial disease. Bates and others went on to explain that if an individual has knowledge on malaria transmission, its clinical features and the appropriate use of the drugs then they are able seek appropriate prevention measures and treatment( (Bates et al., 2004). Adult literacy rate in Tanzania from 15 years of age and above is at 72, 9 and for women alone it’s at 67, according to World Bank in 2009.Majority attend primary education and a few attend secondary and tertiary education. Due to poverty, majority of the parents cannot afford to pay fees so most girls drop out to get married. (UNESCO, 2009).Most women in Tanzania despite being uneducated, have the knowledge on appropriate preventative measures. Some mothers in a community in Ethiopia with limited educational background were able to make a marked decline in mortality rate among children below 5 years of age when they were given information about appropriate anti malarial drugs(Kidane&Morrow,2000). Nevertheless if you are educated you generally make informed and better decisions. There is also some evidence that educated parents are more likely to seek formal treatment when their child gets malaria symptoms, which will reduce the risk of progression to severe disease (Filmer, 2001)
HOUSEHOLD SOCIAL DETERMINANTS.
Use of ITNs is one of the control measures in the prevention of malarial transmission especially in children. Tanzania is one of the poor countries and majority of the people especially in the rural areas cannot afford the nets. This is worsened if there are many children living in the same house which means a few will benefit and it’s usually the parents. As mentioned earlier, children are more vulnerable to malaria and without the nets the vulnerability is increased. Data from household surveys conducted in 30 malarious African countries between 1998 and early 2002 showed that only Guinea Bissau met the 60% target coverage with ITNs defined for Africa in the Abuja Summit on Roll Back Malaria in 2000(Monasch et al, 2004).In 23 countries, Tanzania included ITN use for children under five years old was at or less than 5%, with an overall median use of 2%(Monasch et al, 2004). It is interesting to note that in some communities with high ITN use rate, reason was that they had a small household size with two or less under fives sharing their parents’ bed (Ordinioha, 2007). Having many children in a house result in many of them sleeping on the floor without ITNs and smaller families are more likely to afford ITNs.Another element noted was that large household size tend to be overcrowded and this result in higher concentrations of carbon dioxide and other chemicals which attract mosquitoes and the probability of mosquitoes infecting more than one person during the same night is also higher(Ghebreyesus,et al,;2000).In conclusion a larger household size increase vulnerability to malaria.
Malaria is strongly associated with poverty as parasite prevalence is known to be higher in poorer populations in rural areas (WHO, 2012). People with low socioeconomic status are at a greater risk of malaria infection, 58% of the cases occur in the poorest which is 20% of the world’s population .Besides being at a greater risk they also receive the worst care and endures the severest economic consequences from their illness(Breman,et al,2004).There is a strong relationship between wealth and treatment-seeking behaviour at household level, with children from richer families being more likely to seek medical care and appropriate treatment(Filmer et al,2001).Ownership of bed nets is more common among wealthy households and is closely linked to socioeconomic status(Hanson et al,2000). Several studies in Gambia, Congo and Cote d’Ivoire have shown the relationship between socioeconomic status and risk of malaria transmission. In the Gambia, Clarke and colleagues(Clarke et al,2001) found that the prevalence of malaria declined significantly with increasing wealth, from 51% in the children in the poorer families to 33% in the wealthier households. In the Congo,Tshikuka and others(Tshikuka et al.1996) found that malaria prevalence was higher in two low socioeconomic status area(77 and 69% versus 34%).In Cote d’Ivoire,Henry etal.(Henry et al,2003) found that age standardised annual malaria incidence rates were higher in low socioeconomic status communities(0.8 and 0.9 versus 0.6).
The term ‘gender’ refers to the different behaviour, roles, expectation, and responsibilities all women and men learn in the context of their own societies. Women and men of different ages, marital status, and socioeconomic status have different vulnerabilities influenced by a complex interaction of social, economic, and institutional factors. Gender can therefore affect disease exposure as well as treatment-seeking behaviour and adherence to treatment (Bates et al, 2004). Stereotyped gender roles can also influence how women and men are treated by the health-care system during diagnosis and treatment processes and therefore their vulnerability to progressing to severe disease((Bates et al., 2004).
Gender disparities in social norms like men in India sitting outside in the evenings or occupations such as male loggers in Thailand can cause increased exposure to malaria (Vlassoff&Bonilla, 1994).Most women in Tanzania are prepared to invest in preventative measures such as mosquito nets than men but they don’t have the finances or decision making power to do so. They lack control over household resources and this hinders their ability to seek malaria prevention control measures and treatment.(Lampietti et al,;1999;Livingstone,2003) .Women are also primary care givers to malaria afflicted relatives and children and this has a huge impact on their livelihoods, they end up not having time to take care of themselves or seek treatment .They also have high chances of getting malaria because it can be transmitted from the sick relative to them(Tolhurst &Nyonater,2002). This combination of factors tends to make women more vulnerable than men to the consequences of malaria (Bonilla&Rodriguez, 1993).
COMMUNITY SOCIAL DETERMINANTS
Access to quality health services
Health service weaknesses contribute to high costs in many countries, and include low coverage, user charges, and poor quality of care. Solutions lie in expanded access to high quality, carefully supervised preventive and curative health services (Breman et al, 2004). In the rural areas of Tanzania there is poor quality of health services which includes the inaccurate diagnosis of malaria, lack of skilled health workers and unnecessary use of antimalarial drugs. Vulnerability to malaria in these areas is very high with poor health outcome .It is widely recognized that accurate laboratory-based diagnosis of malaria is central to guiding proper clinical decisions and reducing the use of unnecessary antimalarial drugs (WHO 2004;Wongsrichanalai et al., 2007). Despite this fact, the quality of malaria diagnosis at health care facilities in rural Tanzania is generally poor due to limited skill of laboratory personnel and a lack of essential supplies (Ishengoma et al., 2010). Therefore malaria treatment in most remote areas is based on clinical judgement. According to NMCP, up to early 2009, 83% of health facilities in Tanzania had no laboratory diagnostic capacity for malaria in terms skilled labour force and equipment. Patients were at risk of inaccurate malaria microscopic diagnosis and hence misdiagnosis (Wongsrichanalai et al, 2007)
People living in urban areas are on average 10 times less likely to receive an infective bite than their rural counterparts (Hay et al. 2000; Robert et al. 2003; Trape et al. 1992) .Malaria transmission in the urban area of Dar es Salaam, capital city of Tanzania, is less intensive than in peri-urban and rural areas. This is illustrated by significantly lower percentages of school children infected with malaria parasites (mainly P. falciparum) and living in the urban areas (2’10%) compared with those living in the peri-urban and rural areas ,40% and 70%, respectively.( JICA, unpublished data). The number of breeding sites for malaria vectors in urban areas is reduced because of well-structured drainage networks during malaria control programs (Utzinger etal, 2002; Knudsen etal, 1972).This has protective effect on urban dwellers. Another difference is the dissemination of information about malaria to people in rural and urban areas. A large proportion of the rural population in Tanzania have limited access to information about the signs and symptoms of malaria, risk groups, need for immediate malaria treatment, and malaria prevention techniques (Robert et al, 2003). Thus there is an urgent need for the intensification of communication on malaria in the rural areas. All these factors result in increased vulnerability of malaria in rural population.
Lack of trained health personnel
Another challenge is the human resource crisis in the health sector in Tanzania. It is estimated that currently there is a 65% gap, which means that only 35% of qualified staff are available (Makundi et al, 2005), if we compare the available personnel and Ministry of Health minimum required standards (Ministry of health, 1999) .Tanzania has the lowest ratio of health personnel per capita in sub-Saharan Africa (Oystein et al, 2005).There is urgent need for training and recruitment of skilled personnel. This situation was worsened by the burden of HIV/AIDS which claimed lives of some health workers and also doubling of duties of the few trained health workers available. In some rural health facilities especially the remote areas of Tanzania there are no qualified staff, simply because workers prefer working in urban areas where there is good infrastructure. Another explanation could be lack of motivation in terms of incentives to lure them to rural areas. At district level, malaria control programmes are under district health officers who also have other duties, so in situations of other health crisis, malaria interventions are given low priority (Makundi et al, 2007).Overally, lack of trained health personnel in Tanzania has had a negative impact on the health outcome of the population.
Quality or type of housing
It has long been established that the transmission of many vector-borne diseases is facilitated by house designs that favour mosquito entry(Webb,1985;Kumar et al,2004) and that housing improvements and screening have made substantial contributions to the control and elimination of malaria vectors in many richer countries(Lindsay et al,2002). Therefore, understanding house risk factors that are associated with reduction of indoor mosquito bites and disease transmission in different settings is crucial for disease vector control and elimination. Several studies have identified and documented various house characteristics associated with mosquito entry. Presence of eave gaps, lack of a ceiling and lack of screening over windows and doors proved to be the major contributors to mosquito entry (Lindsay&Snow, 1988). Furthermore, it has been shown in a randomised control trial that blocking all potential house entry points for mosquitoes substantially reduces vector densities and entomological inoculation rates (EIR) (Kirby et al,2009).
A recent study in Northern Tanzania had findings which were consistent with the above studies. It showed a strong association between type of housing and malaria transmission. Houses made of mud walls and grass roots had an increased risk of mosquito bites indoors, such houses created a favourable environment of the resting mosquitoes. Another thing is that they have crevices used by mosquitoes to enter unlike cement walls and metal roofs. Smaller houses with relatively low numbers of windows, doors and rooms were associated with high densities of mosquitoes. It was assumed that smaller houses are likely to concentrate more human odours, which would attract high number of mosquitoes (Lwetoijera et al, 2013).
NATIONAL SOCIAL DETERMINANTS
Malaria is a major cause of poverty and slows economic growth by up to 1’3% per year in endemic countries (Sachs&Malaney, 2002).A nation of low GDP and widespread poverty has limited resources for malaria prevention. Spraying programs have proved effective in lessening the disease’s impact however they require resources to implement. Death rates also rise because drug treatment costs money. Individuals incur debt quickly when dealing with medical costs, drug fees, and multiple family members being repeatedly infected with the disease. When the work force is continually on medical leave and government is drained of resources attempting to combat the disease, an economy will suffer. That is why it has been difficult to eliminate malaria in Tanzania because all the prevention programs require millions of dollars to be effective and yet the country is one of the poorest with low GDP. The burden of malaria is greatest especially among the poor, given the vicious circle of poverty and ill health (Sachs&Malaney, 2002).
Policies on social determinants of health are important because they help to reduce health inequities. With policies in place it is possible to(i) identify programmes which target disadvantaged populations ,for example under fives or pregnant women;(ii)close gaps between the poor and the rich people and( iii)address the social gradient across the whole population( WHO,2010).
The National Malaria Control Program (NMCP) in Tanzania proposes policies and guidelines to the Ministry of Health and Social Welfare through the Malaria Advisory Committee (NMAC) (Ministry of Health, 1999). Policy of decentralization of malaria interventions to district levels has not been effective due to weak health systems and limited capacity (Makundi et al, 2007). The process of decentralization was meant for districts to have more power in decision making in terms of malaria programmes so as improve the quality of health service in the community and also decrease the burden of malaria in the rural population. Morbidity and mortality due to malaria in the communities of Tanzania remains high because of poor interventions.
Another health policy was use of effective control tools such as ITNs as part of the NMCP strategic plan. Emphasis was on children under 5 and pregnant women since they were the most vulnerable groups. However, coverage has so far been low, which indicated that less than 15% of households were using ITNs(Tanzania National Bureau of Statistics(TNBS),2005).This figure is low which shows that majority of the population are not using ITNs as one of the major control programs so vulnerability to malaria remains high especially to the riskier groups.
Another strategy to mitigate against malaria burden in Tanzania was establishment of training centres to address the burden of malaria. Centre for Enhancement of Effective Malaria Interventions (CEEMI) was established in 2001 to strengthen the capacity for malaria control through training by providing needed skills for identifying and solving malaria control problems. So far, the CEEMI has undertaken a number of training sessions involving district health officers as focal persons for control activities(Ijumba&Kitua,2004).It is important to note that the CEEMI has undertaken malaria seminars to sensitize members of parliament in Tanzania to increase advocacy for malaria control initiatives by policy makers, thereby increasing financial resources from the national budget that target malaria activities(Makundi et al,2007).This will improve the health outcome of the population in terms of more trained health personnel, more knowledge and increased awareness of policy makers.
Introduction of artemisinin-based combination therapy (ACT) is another challenge facing Tanzania. Tanzania introduced ACTs in November 2006. One key issue is the cost of ACTs, which are 20 times higher than the cost of conventional therapies ($2.44 per adult dose). The monthly income in a study in a rural area in Tanzania was $13(Jewett et al, 2000). This finding shows that the amount spent on malaria treatment is 10% of the total household income (Tanzania National Bureau of Statistics, 2000/2001).This means that the poor are at a greater disadvantage as they can’t afford the drug. Other conventional drugs e.g. Sulphadoxine pyrimethamine (SP) have 20-60% resistance to malaria and this has never been observed in ACT (Breman et al, 2004).It has been highlighted by many investigators that the cost effectiveness of ACT can be appreciated over a certain time period of 5, 10 or 15 years.Besides being expensive there are also other issues regarding shortages of ACT which are high demand and limited production. This shortage was reported by the World Health Organization in 2004, and Knuming Pharmaceuticals in Yunnan, People’s Republic of China, the only supplier of artemether, indicated that it could not produce enough of this drug to cope with the increasing demand (Makundi et al, 2007).It is best to ensure that ACT is available and affordable with the help of the government before advocating a policy because poor people will end up being disadvantaged.
Politics in Tanzania manipulates allocation of resources to suit particular interests. This allocation depends on who is in power and what are his/her interests. Resources are never allocated according to priority. During elections many promises are made and health facilities are built in many constituencies to suit the needs of the electorate. There is no coordination between construction activities and recruitment of qualified personnel .This result in districts being forced to distribute the existing limited human resource to new facilities. Workers are therefore overburdened and some facilities will end up functioning with unqualified or inadequate health personnel. Policy makers should work with researchers who have authentic data to support their decisions and avoid mere promises merely to impress people. Correct and timely information must be available to all participants, including communities, the media, and political leaders, to avoid distortion of information in the implementation of interventions. A certain level of diplomacy is required in handling the politics of disease control programs (Makundi et al, 2007).
Media can increase vulnerability to malaria of certain populations if not controlled. For example, there is aggressive advertising on the use of ITNs in Tanzania which may give a misconception that the use of ITNs is 100% effective. It should be clearly indicated that other strategies are equally important (Makundi et al, 2007).Data from the Tanzania Demographic and Health Survey in 2005 indicate that coverage of ITNs is 14% in rural areas and 47% in urban areas (National Bureau of Statistics, 2005).The use of ITNs should be emphasized, but the same emphasis should be made for other PMC strategies including indoor residual spraying (IRS).
GLOBAL SOCIAL DETERMINANTS
One of the factors contributing to the increasing burden of malaria is human migration into Tanzania. Malaria transmission has been shown also to be related to human population movement from low risk areas to high risk areas and vice versa. Reports of the malaria burden in Tanzania are increasing and are originating from places thought to be free of malaria, such as the southern and northern highlands (Makundi et al, 2004).Some factors that cause people to move most often, such as environmental deterioration, economic problems, and natural disasters, greatly affect the poor. Understanding and identifying the influence of population movements can improve prevention and control programs.( (Makundi et al., 2007)
Human population movement (HPM) from higher transmission areas result in reintroduction of malaria in malaria-free zones, thereby undermining elimination efforts (Cohen et al, 2012). In non-elimination settings, its best to understand the parasite pattern, the origin of the imported infections and the hot spots of transmission in order to plan effective control measures (Wesolowski et al,2012).In addition to all these problems,HPM has contributed a lot in spreading the drug resistant parasite strains(Lynch&Roper.2011;Roper et al,2004). Strategic control and elimination plans should therefore be built on a strong evidence base including information on HPM and likely parasite movement volumes and routes (Wesolowski et al, 2012). Moreover, identifying key demographic groups most likely to carry infections can provide useful information for tailored and targeted intervention and surveillance efforts (Cotter et al, 2013).
Global financing aid
Tanzania is one of the poorest countries as mentioned earlier on and relies on donor funding. It is worth noting that for some years in Tanzania, there has been a growing global and national political commitment to mitigate against the burden of malaria stimulated by the Roll Back Malaria Partnership and the Global Fund to fight acquired immunodeficiency syndrome (AIDS), tuberculosis (TB), and malaria( ).This has improved majority of the people’s health outcome. Giving an example of how malaria control programmes were financed in Zanzibar, popular resort area in Tanzania in trying to help eliminate malaria. Morbidity and mortality in Zanzibar was reduced by 75% in 2009 compared to 2000-2004.This drop was as a result of scale-up of ITNs,IRS and ACT from 2004 with the help of PMI,USAID,UNICEF,WHO,Global Fund and World bank. They donated half a million ITNs during 2007-2009 enough to replace old nets for the entire population at risk, implemented IRS for several rounds protecting 90% of the population and also delivered ACT. Detailed funding information was not provided but expenditure on malaria in 2009 was US$ 450 000 mainly funded by PMI and UNICEF(World malaria report.2010).The only problem comes when they pull out or when they decide to give priority to other programmes like HIV/AIDS interventions. This is a huge problem when it comes to Tanzania because it relies on donor funding and this affects the health outcome of the poor because they rely on the government which is already poor.
Tanzania has high morbidity and mortality rates caused by malaria. Effective preventative control measures have been set up by the National Malaria Control Programme (NMCP) in trying to mitigate the burden.However, there are many challenges facing the country in terms of implementation and intervention of the programs. Given the renewed global and national commitment efforts to fight malaria, there is still hope for Tanzania to eliminate Malaria.
Hay S. I., Rogers D. J., Toomer J. F., Snow R. W. Annual Plasmodium falciparum Entomological Inoculation Rates (EIR) across Africa: Literature Survey, Internet Access and Review. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2000; 94:113’27. [PMC free article] [PubMed]
Robert V., MacIntyre K., Keating S., Trape J. F., Duchemin J. B., Wilson M., Beier J. C. Malaria Transmission in Urban Sub-Saharan Africa. American Journal of Tropical Medicine and Hygiene. 2003; 68:169’76. [PubMed]
Trape J. F., Lefebvre-Zante E., Legros F., Ndiaye G., Bouganali H., Druille P., Salem G. Vector Density Gradients and the Epidemiology of Urban Malaria in Dakar, Senegal. American Journal of Tropical Medicine and Hygiene. 1992; 47:181’89. [PubMed]
WHO 2014 fact sheet number 94, updated December 2013
Sachs J & Malaney P. “The economic and social burden of malaria”. Nature Magazine, 2002<http://www.nature.com/nature/journal/v415/n6872/full/415680a.html? Lang=en>
WHO 2010.Policy recommendation on intermittent preventive treatment during infancy with sulphadoxine-pyrimethamine (SP-IPTi) for plasmodium falciparum malaria control in Africa.
Bates,I.,Fenton,C.,Gruber,J.,Lalloo,D.,Lara,A.M.,Squire,S.B.,Theobald,S.,Thomson,R.&Tolhurst,R.2004.Vulnerability to malaria, tuberculosis and HIV/AIDS infection and disease. Part 1: determinants operating at individual and household level. The Lancet infectious Diseases, 4,267-277.
Breman, J., Alilio, M. &Mills, A.2004.Conquering the intolerable burden of malaria: Whats new, whats needed: A summary. The American society of tropical medicine and hygiene, 71, 15
WHO 2010a.Policy recommendation on intermittent preventative treatment during infancy with sulphadoxine-pyrimethamine (SP-IPTi) for plasmodium falciparum malaria control in Africa.3.
WHO 2010b.WHO social determinants of health framework.
World Malaria Report 2009. Geneva, World Health Organization, 2009 (ISBN 978 92 4 156390 1) http://whqlibdoc.who.int/publications/2009/9789241563901_eng.pdf
WHO World Malaria Report 2012 .Geneva, World Health Organization, 2012
World Health Organization. Report on infectious diseases: scaling up the response to infectious diseases. Geneva: WHO, 2002.
Trape JF, Rogier C. Combating malaria morbidity and mortality by reducing transmission. Parasitol Today 1996; 12: 236’40.
Sharp PT, Harvey P. Malaria and growth stunting in young children of the highlands of Papua New Guinea. P N G Med J 1980; 23: 132’40.
Kleinschmidt I, Sharp B. Patterns in age-specific malaria incidence in a population exposed to low levels of malaria transmission intensity.Trop Med Int Health 2001; 6: 986’91.
Vlassoff C, Bonilla E. Gender-related differences in the impact of tropical diseases on women: what do we know? J Biosoc Sci 1994; 26: 37’53.
Monasch R, Reinisch A, Steketee RW, Korenromp EL, Alnwick D, Bergevin Y.Child coverage with mosquito nets and malaria treatment from population based surveys in African countries: A baseline for monitoring progress in Roll Back Malaria. Am J Trop Med Hyg 2004, 71 (2 Suppl): 232-8.
Ordinioha B. The use of insecticide-treated bed net in a semi-urban community in south-south, Nigeria. Niger J Med 2007; 16:223-6.
Modiano D, Chiucchiuini A, Petrarca V, et al.Interethnic differences in the humoral response to non-repetitive regions of the Plasmodium falciparum sporozoite protein. Am J Trop Med Hyg 1999; 61: 663’67?
Riley EM, Olerup O, Bennett S, et al. MHC & malaria: the relationship between HLA class II alleles& immune responses to Plasmodium falciparum. Int Immunol 1992; 4: 1055 63.
Aidoo M, Terlouw DJ, Kolczak MS, et al. Protective effects of the sickle cell gene against malaria morbidity and mortality. Lancet 2002; 359: 1311’12.
Hill AV, Allsopp CE, Kwiatkowski D, et al.Common west African HLA antigens are associated with protection from severe malaria. Nature 1991; 352: 595’600.
Steketee RW, Wirima JJ, Slutsker L, et al. Malaria parasite infection during pregnancy and at delivery in mother, placenta, and newborn: efficacy of chloroquine and mefloquine in rural Malawi. Am J Trop Med Hyg 1996; 55: 24’32?
Verhoeff FH, Brabin BJ, Hart CA, Chimsuku L,Kazembe P, Broadhead RL. Increased prevalence of malaria in HIV-infected pregnant women and its implications for malaria control. Trop Med Int Health 1999; 4: 5’12.
French N, Nakiyingi J, Lugada E, Watera C, Whitworth JA, Gilks CF. Increasing rates ofmalarial fever with deteriorating immune status in HIV-1-infected Ugandan adults. AIDS 2001; 15:899’906.
Grimwade K, French N, Mbatha D, Zungu D,Dedicoat M, Gilks CF. Childhood malaria in a region of unstable transmission and high human immunodeficiency virus prevalence. Paediatric Infect Dis J 2003; 22: 1057’63
Shaffer N, Hedberg K, Davachi F, et al. Trends and risk factors for HIV-1 seropositivity among outpatient children, Kinshasa, Zaire. AIDS 1990; 4: 1231’36.
Whitworth J, Morgan D, Quigley M, et al. Effect of HIV-1 and increasing immunosuppression on malaria parasitaemia and clinical episodes in adults in rural Uganda: a cohort study. Lancet 2000; 356: 1051’56.
Filmer D. Fever and its treatment among the more and less poor in sub-Saharan Africa. Washington DC: World Bank Development Research Group, 2001.
Ghebreyesus TA, Haile M, Witten KH, et al.Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg 2000; 94: 17’21.
Tolhurst R, Nyonator F. Developing a methodology for the analysis of gender and malaria. Final Report on TDR Project 990984. Liverpool: Liverpool School of Tropical Medicine, 2002.
Lampietti J, Poulos C, Cropper M, Mitiku H,Whittington D. Gender and preferences for malaria prevention in Tigray, Ethiopia. World Bank Gender and Development Working Paper Series. Washington DC: World Bank, 1999.
Livingstone AM. A study of the links between gender and health in the upper west region: Upper West Region, Ghana: DANIDA/Ministry of Health, 2003.
Bonilla E, Rodriguez A. Determining malaria effects in rural Colombia. Soc Sci Med 1993; 37: 1109’14.
Kidane G, Morrow RH. Teaching mothers to provide home treatment of malaria in Tigray, Ethiopia: a randomised trial. Lancet 2000; 356: 550’55.
Snow RW, Korenromp E, Drakely C, Gouws E, 2004. Paediatric mortality in Africa: Plasmodium falciparum malaria as a cause or risk? Am J Trop Med Hyg 71 (Suppl 2): 16’24.
Van Geertruyden JP, Thomas F, Erhart A,D’Alessandro U, 2004.Malaria as an independent risk factor for perinatal mortality and/or stillbirths: a review and meta-analysis. AmJTropMed Hyg 71 (Suppl 2): 35’40.
Ministry of Health Government of Tanzania, 2003. National Malaria Control Program; National Malaria Medium Term Strategic Plan, 2003’2007. Dar es Salaam, Tanzania: Ministry of Health.
National Bureau of Statistics, 2003. Tanzania Household Budget Survey 2000/01. Dar es Salaam, Tanzania: Government of Tanzania; 1’115.
Ministry of Finance Tanzania, 2001. Tanzania National Health Accounts 2000. Dar es Salaam, Tanzania: Government of Tanzania.
Jewett M, Miller N, Mzava N, 2000: Malaria Expenditure Analysis:Tanzania Case Study. Report prepared for DFID andRBM, 1’54.
Lindsay SW, Emerson PM, Charlwood JD, 2002. Reducing malaria by mosquito-proofing houses. Trends Parasitol 18: 510’514.
Makundi EA, Manongi R, Mushi A, Ib CB, Theander T, Ronn A,Alilion MS, 2005. The use of nominal group technique in identifying community health priorities in Moshi rural district, northern Tanzania. Tanzania Health Res Bull 7: 133’141.
National Bureau of Statistics (Tanzania) and ORC, 2005. Tanzania Demographic and Health Survey, 2004’2005. Calverton MD: National Bureau of Statistics and ORC.
Ministry of Health Tanzania, 2004: Regional Medical Officers Conference, Proceedings. Dar es Salaam, Tanzania, 1’98.
Ijumba JN, Kitua AY, 2004. 2004: Enhancing the application of effective malaria interventions in Africa through training. AmJ Trop Hyg 71 (Suppl 2): 253’258.
Makundi EA, Hiza P, Mcharo J, Senkoro K, Kamugisha W, Mubyaz G, Kisinza W, Kalinga R, Mdoe M, Kwesi E, Rubona J,Simba R, Makwaya S, Kitua A, 2005. Assessing Trends in the Health Sector: Experience from 10 Districts. Dar es Salaam,Tanzania. Report submitted to the Ministry of Health of Tanzania.
Ministry of Health Tanzania, 1999: Minimum Staffing Levels Requirements.Dar es Salaam, Tanzania: Government of Tanzania.
Ministry of Health Tanzania, 2003. Guidelines for Disbursement of Basket Funds at Different Levels. Dar es Salaam, Tanzania:Government of Tanzania.
Tanzania National Bureau of Statistics, 2000/2001. Household Budget Survey. Available from http.www.Tanzania.go.tz./hbs/home page.
Oystein OE, Ndeki S, Norheim OF, 2005. Human resources for emergence obstetric care in northern Tanzania: distribution of quality of quantity? Hum Resour Health 3: 1478’1491.
Webb DJ: Low-cost housing and parasite vectors. Parasitol Today (Personal ed) 1985, 1(2):65.
Kumar DVR, Krishna D, Murty US, Sai KSK: Impact of different housing structures on filarial transmission in rural areas of southern India. South East Asian J Trop Med Publ Health 2004, 35(3):587’590.
Kirby MJ, Ameh D, Bottomley C, Green C, Jawara M, Milligan PJ, Snell PC,Conway DJ, Lindsay SW: Effect of two different house screening interventions on exposure to malaria vectors and on anaemia in children in The Gambia: a randomised controlled trial. Lancet 2009, 374:998’1009.
Lindsay SW, Snow RW: The trouble with eaves; house entry by vectors of malaria. Trans R Soc Trop Med Hyg 1988, 82:645’646.
Makundi,E.A.,Mboera,L.E.G.,Malebo,H.M&KITUA,A.2007.Priority setting on Malaria interventions in Tanzania: Strategies and challenges against the intolerable burden. The
American Society of Tropical Medicine and Hygiene, 77, 6.
Cohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, Moonen B:Malaria resurgence: a systematic review and assessment of its causes.Malar J 2012, 11:122.
Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, Buckee CO: Quantifying the impact of human mobility on malaria. Science 2012,338:267’270.
Lynch C, Roper C: The transit phase of migration: circulation of malaria and its multidrug-resistant forms in Africa. PLoS Med 2011, 8:e1001040.
Roper C, Pearce R, Nair S, Sharp B, Nosten F, Anderson T: Intercontinental spread of pyrimethamine-resistant malaria. Science 2004, 305:1124.
Cotter C, Sturrock HJW, Hsiang MS, Liu J, Phillips AA, Hwang J, Gueye CS,Fullman N, Gosling RD, Feachem RGA: The changing epidemiology of malaria elimination: new strategies