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CHAPTER 3: ANALYSIS OF THE NUTRITION SITUATION AND TRENDS IN TANZANIA


Introduction
Sources of information and reliability of data
The public health significance of malnutrition in Tanzania
Protein Energy Malnutrition (PEM)
Micronutrient Malnutrition
Diet and nutrition related non-communicable diseases
Malnutrition and the Mortality pattern

Introduction

The 1980’s represent a unique period in the history of nutrition in Tanzania. It is a period when most nutrition information was collected. It can also be said to be the period when a critical level of political and decision makers awareness and commitment on nutrition intervention was possible to be mobilized on a large scale. Unlike in other decades the 1980s was characterized by possibilities for multi-sectoral and multidisciplinary nutrition intervention. The decade also boasts of a large number of nutrition related policies and programmes and for the first time in the history of Tanzania there were hopes that the apparently intractable problem of protein-energy malnutrition could be significantly reduced without waiting for the trickle down effects of a high social economic development. Despite being the most economically crisis decade since independence in 1961, notable reductions in malnutrition were observed. It is against this background that the nutrition situation during the decade will be reviewed.

Sources of information and reliability of data

There is considerable information describing the nutrition situation in different parts of Tanzania, based on spot surveys, child growth monitoring systems or research work. Most of this information is not nationally representative and is focused more on children under-fives than other population groups. Only scanty serial national information is available making it possible to discern trends over time in only a few indicators and in specific areas of the country.

However, this situation is slowly changing. The collection of infant and child mortality data, parameters which are closely associated with the rates of malnutrition are in the process of been incorporated into the MCH system in order to obtain trends over a shorter period of time. Presently, such data is obtained from the national census which takes place every ten years and another three to four years for the mortality data to be available. For example the 1988 population data which provide the most comprehensive picture of infant and child mortality by the Bureau of Statistics and estimates by region and district were made available at the end of 1992. Thus efforts to obtain more regular child mortality information by using the Brass-Macrae “previous birth technique” has already been pilot tested in Mwanza region and found to be reliable for estimates of mortality among under-five year old children. Pilot testing of a revised MCH reporting system which has incorporated the previous birth question has been conducted in Mbeya and its possible expansion to the whole country’s MCH system is envisaged if an evaluation of the pilot test finds it possible.

The only nationally representative data on protein-energy malnutrition in children under-five years old has recently been obtained from two sources. The first is the nutrition module incorporated by TFNC into the Household Budget Survey (HBS) conducted by the Bureau of Statistics with analyzed information being available for the December 1991 to April 1992 period. The second is the Demographic and Health Survey (DHS) conducted by the Bureau of Statistics in October 1991 to March 1992. Reports for both the HBS and DHS are preliminary and the figures presented are provisional. The principal reports will be completed in 1993 and the final figures are not expected to differ markedly from those presented in this report.

Thus the sources of information on which this review will be based comes from the DHS and HBS, the data base at the Tanzania Food and Nutrition Centre (TFNC), the monitoring systems from the Iringa Nutrition Programmes (INP), Zanzibar Nutrition Programme (ZNP), the Child Survival and Development (CSD) programmes and the Census data from the Bureau of Statistics. Information from the Health Information System (HIS), Essential Drug Programme (EDP) and Maternal Child Health (MCH) from the Ministry of Health has been used from a variety of evaluation reports. Data has also been obtained from documents published by the Ministry of Agriculture and Livestock Development, Muhimbili Medical Centre and published and unpublished researches done by various institutions and individuals. It should be borne in mind that the objectives for the collection of the various data to be cited were different and thus the quality of some of it should be taken with caution. For example, the nutrition information systems in the Iringa Nutrition Programme (INP) and its extended Child Survival and Development (CSD) Programmes were designed primarily to catalyze the triple-A cycle at the household, village, and higher levels, although there has also been great interest and use of its potential to assist in the evaluation of the programmes’ impact on nutritional status. The DHS and nutrition module of the HBS give more accurate and representative information but only at the national level, as the primary objectives were to get good quality national data for decision making. However, the consistency in the trends from the different sources of data leave no doubt that positive progress is being made. The DHS and HBS data give figures which are lower than previous estimates. In the community based growth monitoring systems, consistently high rates of attendance and reduced trend rates of malnutrition are being reported. Efforts are being made to streamline the compilation and reporting systems so as to improve the quality of the data and its timely availability for use in decision making. The indications are that, monitoring systems for the achievements of the nutrition goals for the 1990s would be set in place by the end of 1993.

The public health significance of malnutrition in Tanzania

Tanzania’s main problems of nutrition are similar to those of other countries in Sub-Saharan Africa. They are related to undernourishment and are:- protein-energy-deficiency (PED), iron deficiency anaemia (IDA), iodine deficiency disorders (IDD) and vitamin A deficiency (VAD).

Apart from these deficiency disorders, there are two nutrient excess disorders represented by fluorosis in the northern and north-western and central parts of mainland; and the problem of overweight, obesity and diet-related non-communicable diseases which seem to be increasing especially in the urban elite and business sections of the community emulating unhealthy food habits and lifestyles.

National estimates for the magnitude of malnutrition were done in 1987. These are shown in tables 4 and 5.

The estimates were based on various surveys which had been conducted in different parts of the country until then. Average prevalence rates for the various nutritional problems were calculated and then weighted against the national population group being considered. The weighted rates for each population group were then added to give the national picture for all age groups. These figures are being revised to take into account the positive changes which have occurred as a result of the implementation of specific and broad based nutrition programmes.

It should be observed that all the nutritional deficiencies affect mainly under-five year children and pregnant and lactating women. It should, however be pointed out that lactation protects women against anaemia for the period they are not menstruating.

Protein Energy Malnutrition (PEM)


Nutritional status of children aged under five years
Nutritional status of school age children
Nutritional status of adolescents
Nutritional status of adults
Maternal malnutrition

Chronic energy deficiency is the most widespread and elusive nutritional problem in Tanzania. Normally, when the energy requirements are met from a mixed natural staple food, the protein and micronutrient requirements would also be met.

Table 4: National Estimates of the population affected by the Major nutritional deficiencies in Tanzania out of an estimated population of 25.0 (1987)

Type of Deficiency

Severe

Moderate

Total

No.

%

No.

%

No.

%

1. Protein Energy Deficiency

0.7

3.0

5.6

25.0

6.3

28.0

2. Anaemia

1.6

7.0

5.6

25.0

7.2

32.0

3. IDD (measurable)

0.6

3.0

5.0

22.0

5.6

25.0

4. Vitamin A deficiency

0.02

0.1

1.4

6.0

1.4

6.1


Where:-


PED:

severe = weight for age(W/A) less than 80% of NCHS reference for children under-fives and Body Mass Index (BMI) of less than 18.5 for adults.


moderate = W/A between 60 - 80% of NCHS reference and BMI of 18.5-<20

Anaemia:

severe = Haemoglobin (Hb) less than 8.5 g/dl


moderate = Hb of >8.5 - 10 g/dl

IDD:

severe = cretins and cretinoids or sub-cretins, visible goitre or urinary iodine excretion less than 25 ug/dl


moderate = non-visible goitre or urinary iodine excretion of 25-50 ug/dl

Vitamin A deficiency:

severe = serum retinol less than 10 mg/dl


moderate = serum retinol 10 - <20 mg/dl

Source: Kavishe F.P (1987): The Food and Nutrition Situation in Tanzania. TFNC Report No. 1215
Table 5: The prevalence of various forms of nutrient deficiencies in Tanzania according to population groups

Category of population affected

Type of deficiency and percent affected

PED

Anaemia

IDD

VAD

Children under-five years

52.0

45.0

13.0

30.0

Pregnant and lactating women

13.0

80.0

52.0

0.7

Remaining groups

20.0

20.0

40.0

0.1

General population

28.0

32.0

25.0

6.1

Source: Kavishe F.P (1987), TFNC Report no. 1215
The methods for the assessment of protein-energy nutritional status vary with the age groups being considered, since each stage of human life has different nutritional considerations related to physiological needs, susceptibility to infection, and cultural benefits or hazards. Moreover, the risk of the different forms of malnutrition varies in different age groups. For example the anthropometric effects of protein-energy under-nutrition in rapidly growing young children plainly will be different from those of adults for whom physiological growth has ceased. Again, the reproductive burden carried by women means that pregnant women are a nutritionally vulnerable group. Differences in nutritional problems and manifestations make it useful to consider the following categories in nutritional assessment:- infant and young child nutrition; nutrition of school age children, nutrition of adolescents and adult nutrition.

Nutritional status of children aged under five years

Measures of child nutritional status using attained weight and height are commonly used to assess the overall nutritional and health status of children. The anthropometric indicators which will be used in this section and their interpretation are as follows:-

Chronic malnutrition


Height-for-age (H\A) below -2SD

.....Stunting



Current or acute Malnutrition


Weight-for-height (W\H) below -2SD

.....Wasting



Chronic and current malnutrition


Weight-for-age (W\A) below -2SD

.....Underweight


Underweight as measured by low Weight-for-Age reflects both recent and long-term malnutrition, especially in children older than two years. This is the commonest measure which has been used in monitoring growth and nutrition trends.

Clinical malnutrition

The data from the routine MCH assessments which use weight-for-age, is unfortunately not reliable due to biases and compilation errors. The biases in the MCH data derive from an over-representation of the under one year age group who because of the widespread breast-feeding at that age tend to be better nourished than all children under-five on average. This over-representation is brought about by the fact that parents tend to send their children to the MCH clinic for vaccination, and after completing vaccination at the age of 9 months when they get the measles vaccination; the children are not sent to the clinic again as often. Moreover, parents sending their children to the MCH clinics tend to be better informed, better educated and would have children with a better nutritional status.

The compilation errors arise as a result of a faulty system in the generation of the data. Though all children are counted to get the denominator for the calculation of various rates; not all children are weighed. The result is that the calculation of malnutrition rates by the MCH staff uses denominators which are high and this biases downwards the estimates of child malnutrition rates. As a result, the rates of malnutrition derived from the MCH system systematically underestimates the malnutrition rates as derived from community based surveys by a factor of about three to five. This anomaly has already been brought to the attention of the Ministry of Health for correction.

One useful information derived from the data collected through the MCH system is the classification of the clinical types of malnutrition. An analysis of data from seven regions implementing the CSD programmes from 1985 to 1988 (table 6) show that marasmus is the predominant clinical form of PEM.

The prevalence of marasmus is about 1 percent which is roughly about twice the rate of kwashiorkor which is around 0.5 percent. While we can ignore the prevalence of underweight of about 8 percent as been unrepresentative of the community situation; the prevalence of the severe clinical forms (kwashiorkor and marasmus) seem to reflect the true community situation since they are perceived as diseases and are more likely to be reported.

Table 6: Prevalence of malnutrition for selected regions as reported in the Maternal and Child Health (MCH) growth monitoring system; 1985 - 1988

Region

Year

Prevalence (%) among children attending

Underweight

Kwashiorkor

Marasmus

Iringa

1985

10.3

0.6

1.1

1986

9.4

0.3

0.9

1987

9.5

0.4

0.7

1988

11.6

0.3

0.6

Kagera

1985

13.1

1.1

1.7

1986

8.7

0.6

1.3

1987

12.6

0.8

1.6

1988

10.5

0.7

1.7

Mtwara

1985

6.0

0.3

1.0

1986

4.1

0.2

0.7

1987

6.0

0.9

0.6

1988

5.3

0.4

0.6

Ruvuma

1985

6.6

0.3

0.6

1986

6.6

0.3

0.6

1987

7.4

0.3

0.5

1988

8.0

0.2

0.5

Kilimanjaro

1985

6.8

0.6

1.1

1986

7.2

0.5

1.2

1987

6.3

0.4

0.7

1988

7.0

0.4

1.4

Morogoro

1985

6.4

0.5

0.7

1986

5.5

0.3

0.7

1987

6.0

0.2

0.6

1988

8.0

1.3

1.3

Shinyanga

1985

6.9

0.3

0.6

1986

6.1

0.5

0.8

1987

5.3

0.4

0.6

1988

6.9

0.4

0.6

Average

1985

8.0

0.5

1.0

1986

6.9

0.4

0.9

1987

7.0

0.5

0.8

1988

8.4

0.5

1.0

Source: UNICEF/TFNC reports, quoted in TFNC report no. 1322
Anthropometric indicators and trends

Anthropometric indicators show that stunting (low height-for-age), the chronic form of under-nutrition is the commonest form of malnutrition in Tanzania (Table 7) and that at the national level, the prevalence of underweight (low weight-for-age), the commonest anthropometric measurement which has been used in Tanzania, shows a declining trend.

Table 7a: Indicators of under-nutrition in children under-live years of age (3 - 60 months) in Tanzania (1985 - 1992) using a cut-off level of -2 Standard Deviation

Type of PEM

1985 estimates

1987 estimates

HBS (Dec 1991-April, 1992)

DHS (October 1991-March, 1992)

Below -3SD

Below -2SD

Below -3SD

Below -2SD

Stunting (H/A) (chronic)

n.a

n.a

12.3

40.5

19.8

46.7

Wasting (W/H) (Acute)

n.a

n.a

1.2

8.5

1.2

5.6

Underweight (W/A)

40 - 60

52

5.3

25.2

7.1

28.8

Source: URT & DHS Macro International Inc. (Tanzania DHS 1991/92); URT/UNICEF, 1985; and TFNC 1993.
Both the DHS and HBS data were collected about the same period. For HBS it was the December 1991 - April 1992 and for the DHS it was October 1991 - March 1992. According to previous studies (FAO/TFNC 1992) this is the peak malnutrition period because it is associated with high disease rates and heavy workload for women as it is the rainy season. The same trained Bureau of Statistics enumerators were used. As expected the mean 1992 annual HBS data of the nutrition module gave even lower results than both the December 1991-April 1992 HBS and DHS data (table 7b).

Table 7b: Comparison of the nutritional status of children (3-60 months) between the HBS annual data for 1992 and DHS data

Anthropometric indicator

HBS mean for 1992

DHS

No.

below -3SD

below -2SD

No.

below -3SD

below -2SD

Stunting (H/A)

2,626

15.1

31.4

6,095

19.8

46.7

Wasting (W/H)

2,691

1.7

7.8

6,095

1.2

5.6

Underweight (W/A)

3,128

5.3

20.4

6,095

7.1

28.8

Source: TFNC data base, 1993 and URT/Bureau of Statistics, TDHS 1993
The similarity between the DHS and HBS data collected around the same period is striking. It is even more striking that ACC/SCN projections using social and economic indicators for Tanzania arrived at an underweight (below -2SD) prevalence rate of 24 percent which is very close to both the HBS and DHS data (Garcia, 1993 personal communication). Severe malnutrition (below -3SD) for both HBS and DHS falls within previous estimates. However, for underweight (W/A) the rates from both data sources show significantly lower rates than previous estimates which may be a reflection of an improving trend or previous overestimates. Both reasons seem plausible.

It might be argued that earlier estimates might have over-estimated the situation because of two reasons. The first is that nutrition surveys tended to concentrate in areas where the situation was bad so that action could be taken. The second reason may be the use of percentages of median cut-off point of below 80 percent of the Harvard Standard which the Road to Health Card of the MCH employs in defining underweight. The difference between using this criteria and the standard deviation criteria is not trivial. By using the standard deviation criteria, the prevalence rates may be reduced down to almost two thirds of the median percent criteria. Since the distribution of percentages of the reference values vary by age and sex, and as an age invariant probability statement can be made using the standard deviation, the latter criteria is preferred.

But even with such a reduction previous estimates and actual measurements in various areas show high prevalence rates of malnutrition above those of the DHS and HBS data which cannot be accounted for by the one third reduction implied in the differences in criteria selection. This indicates that the improvement is real. The improvement trends shown for regions implementing the Child-Survival and Development (CSD) programmes in tables 10 and 11 confirm this conclusion.

The levels of under-nutrition in children under-five years old in Tanzania compare well with those of other Sub-Saharan African countries with similar levels of socio-economic development. The levels are below the average for Sub-Saharan Africa; but unlike the rising trend of malnutrition in Sub-Saharan Africa, the trend for Tanzania is falling. Table 8 presents measures of some anthropometric indicators of under-nutrition in some neighbouring countries. Stunting, the chronic form of malnutrition is the most prevalent form of under-nutrition in all these countries.

If the means of the prevalence rates for the various types of under-nutrition for the nine countries in table 8 are taken as the reference points for comparison, it is only in wasting that Tanzania is better off than the average. Thus for wasting Tanzania is better off than Zambia, Zaire, Rwanda, Malawi, Kenya and Burundi.

It should be noted that for Kenya, wasting is probably only of the mild type, as severe PEM has a very low prevalence. For stunting Tanzania is better off than Zambia, Zaire, Malawi and Burundi. For weight-for-age which measures the combined effect of stunting and wasting, Tanzania is better off than only Zaire, Rwanda and Burundi. It should be noted that with the exception of Zimbabwe which has the best indicators in this comparison, the current levels of malnutrition in Tanzania and in the other countries are very high. Neighbouring countries with better nutrition indicators than Tanzania, like Zimbabwe and Kenya also have better economic indicators. The under-nutrition trends in Kenya, Tanzania and Zimbabwe are falling; those for Zambia are rising while for the other countries they seem to be stable.

Compared to Sub-Saharan Africa; Tanzania is just slightly better than the average of 30 percent for underweight. For developing countries, excluding China, Tanzania is worse off for stunting but better off in wasting and underweight.

Community-based data from selected areas for the 1980-1990 decade (table 9) show a mean underweight prevalence rate of 6.2 percent for severe under-nutrition and 48 percent for total underweight which is very close to earlier estimates of 5 percent and 47 percent respectively.

Table 8: Indicators of under-nutrition in countries neighbouring Tanzania and the developing countries as a group

Country

Year

Stunting (H/A) (%)

Wasting (W/H) (%)

Underweight (W/A) (%)

1. Tanzania

1991/92 (DHS)

46.6

5.5

28.5

2. Zambia*

1990

59.4

10.0

24.7

3. Zimbabwe**

1988

29.0

0.8

11.5

4. Zaire***

1990

46.0

9.6

33.4

5. Rwanda

1980-91

34.0

11.4

36.6

6. Malawi

1980-91

61.0

8.0

24.0

7. Kenya****

1980-91

41.0

10.0

17.0

8. Burundi

1980-91

60.0

10.0

38.0

9. Uganda

1980-91

25.0

4.0

23.0

Mean

1980-92

44.1

7.7

26.3

Sub-Saharan Africa

1990

-

-

30.0

Developing countries (excluding China)*****

1987-1990

39.0

8.0

36.0


Sources:

UNICEF, State of the World’s Children 1992, and

*

Government of the Republic of Zambia, Report of the pilot nutrition module (1990).

**

Demographic Health Surveys (DHS), 1988

***

UNICEF, State of the World’s Children, 1990.

****

Stunting and wasting figures from UNICEF, State of the World’s Children, 1992 and underweight figure from the Rural Nutrition Survey 1987.

*****

ACC/SCN Second Report on the World Nutrition Situation (1992) and Chen (1990)


Table 9: Underweight for age (W/A) in children under-five years old in community surveys in Tanzania (1980-1991) using the Harvard Standard of below 80 percent

Source

Year

Season

District

N

Prevalence of PEM (W\A)

<60%

<80%

As below

1980-1990

All

As below

111,285

6.2

48.0

TFNC

1980

December

Lindi

528

11.0

41.0

TFNC

1981

January

Mtwara

579

9.0

50.0

TFNC

1982

June

Iringa

1,705

4.0

52.0

TFNC

1983

August

Iringa

733

2.0

45.0

JNSP

1984

Apr/June

Iringa

30,000

5.9

62.0

CSD

1985

July

Biharamulo

5,536

10.0

58.0

CSD

1985

August

Ngara

5,731

7.0

57.0

CSD

1987

May

Ruvuma

10,979

7.5

57.0

CSD

1987

October

Hai

4,105

3.5

34.0

CSD

1987

November

Mtwara

8,182

8.0

55.0

CSD

1988

Sept/Oct

Morogoro

6,407

5.2

51.0

CSD/JCGP

1988

Sept

Shinyanga

10,966

3.4

42.0

CSD/Surv

1990

June/July

Singida

11,608

4.6

36.0

CSD/Surv

1990

August

Tarime

5,520

4.1

28.8

CSD/Surv

1990

August

Serengeti

6,287

10.5

46.3

TFNC

1991

October

Nkasi

915

6.0

51.5

TFNC

1991

October

Sumbawanga R

930

4.4

8.1

TFNC

1991

October

Sumbawanga U

574

5.2

49.3

Source: URT/UNICEF 1990 and TFNC (1990)
Moreover, in areas where specific nutrition programmes have been implemented, there is an improving trend in the nutrition situation (table 10).

Table 10: Trends in total underweight (W/A of below 80 percent Harvard Standard) in CSD areas, 1984-1991


1984

1985

1986

1987

1988

1989

1990

1991

Iringa

55.8

44.9

40.7

39.6

38.0

37.3

37.5

36.7

Kagera

-

56.9

50.0

36.2

41.0

33.7

29.2

38.9

Kilimanjaro

-

-

-

34.2

24.7

18.5

16.3

12.2

Mara

-

-

-

-

-

-

38.1

30.2

Morogoro

-

-

-

-

44.6

44.6

35.7

39.4

Mtwara

-

-

-

54.9

48.9

45.8

47.0

41.3

Ruvuma

-

-

-

54.6

50.7

47.9

42.2

38.2

Shinyanga

-

-

-

-

41.9

24.9

28.7

27.1

Singida

-

-

-

-

-

-

35.6

33.4

Source: TFNC and UNICEF nutrition data bases, 1992.
The decline for severe malnutrition is more pronounced (table 11) than for total and moderate under-nutrition.

Table 11: Trends in severe underweight (W/A of below 80 percent Harvard) in CSD areas, 1984-1991

Region/year

1984

1985

1986

1987

1988

1989

1990

1991

Iringa

6.3

3.7

2.2

1.9

1.7

2.4

1.9

1.6

Kagera

-

7.9

6.5

3.9

3.8

1.9

1.4

2.0

Kilimanjaro

-

-

-

3.5

1.4

1.5

0.4

0.3

Mara

-

-

-

-

-

-

7.4

2.3

Morogoro

-

-

-

-

4.3

3.2

2.2

2.9

Mtwara

-

-

-

8.0

5.5

5.8

6.0

3.2

Ruvuma

-

-

-

4.9

5.8

4.4

3.5

4.3

Shinyanga

-

-

-

-

3.4

2.1

1.8

1.3

Singida

-

-

-

-

-

-

2.7

1.7

Source: TFNC and UNICEF nutrition data base, 1992
When the rates of reduction in malnutrition are considered on the basis of the severity and period elapsed (table 12), it becomes obvious that the highest reduction rates are achieved in the severe forms.

Excluding Singida which has only one year programme in the analysis and with the exception of Ruvuma where there is a two percent increase in the rate of severe malnutrition over a four year period, there is a substantial decrease in the severe forms of malnutrition in all other regions ranging from a reduction rate of 32.6 percent in Morogoro to 91.4 percent for Kilimanjaro. On average within a period of four years it was possible to reduce severe malnutrition by more than a half; moderate malnutrition by more than a quarter and total malnutrition by about a third.

While these reduction rates indicate that it is possible for Tanzania to achieve the nutrition goals of the 1990s as far as PEM is concerned; the apparent increase in severe malnutrition in Ruvuma shows at least two things. The first is that though food is a necessary condition for good nutrition, it is not a sufficient condition. Ruvuma is among the “big four” regions in maize production (the others being Iringa, Mbeya and Rukwa). This is an important point to stress as many national, regional and district level decision makers and people in non-CSD areas link nutrition with food self-sufficiency almost to the exclusion of the other factors of care and health. The second is that the CSD programme in Ruvuma was initially weak in the application of the triple A cycle approach as compared to the other regions.

Iringa, Kagera, Kilimanjaro and Shinyanga regions had reduction rates which were above the average calculated for the eight regions (excluding Singida). Taking into account that Iringa and Kagera have the longest integrated community-based nutrition programmes (seven and six years respectively), the reduction rates of less than 50 percent for moderate and total malnutrition after all those years may indicate to the casual observer that the euphoria which accompanied the early successes of the Iringa Nutrition Programme was not sustained. But a deeper analysis of tables 10 and 11 shows that the reasons may be more fundamental and could be applied to all the other regions. First, for all programme areas it appears that the highest reduction rates occur during the first two years of the programme and on average the levels of severe malnutrition seem to level off when a prevalence rate of about two percent is reached. Second, regions with higher reductions rates and, therefore, do not follow this trend are those with initial low rates of malnutrition. These regions are either those with a high socio-economic status (Kilimanjaro) or those which are predominantly pastoral (Shinyanga and Singida).

The implications of these observations are that while initial programme impact may be attributed to actions on the immediate and partly on the underlying causes of malnutrition, the programmes have not to any significant extent addressed the basic problems of poverty and socio-cultural factors militating against nutrition improvement including the issue of gender relations. In other words interventions have concentrated mainly on the delivery of services and to some extent on institutional capacity building and empowerment. Interventions at the level of underlying and basic causes calls for more fundamental institutional and grassroots socio-economic and political reforms. Though these reforms have started at the macro-level, it will take some time for them to significantly permeate to the rural areas.

The overall improvement in the nutritional status which occurred during the 1980s, a decade dubbed as the most hit by the economic crisis presents as a paradox at first sight. A closer scrutiny reveals that the Iringa Joint Nutrition Support Programme (JNSP), the subsequent Child Survival and Development (CSD) programmes which are community-based and the micronutrient control programmes were initiated at about the same time as the Economic Recovery Programme during the second half of the 1980s.

It is of interest to note that “nutrition recovery” started to occur at about the same time as “economic recovery”. Thus while the nutrition situation was described by TFNC as “constant over time and place” during the first half of the 1980s when the economic crisis was most severe; it was described as “appearing to respond to specific interventions” during the second half of the 1980s when the economic growth as measured by GDP was growing at about 3.6 percent. We believe that the economic stimulating effect of both the recovery programme and the policy reforms created a conducive environment for the initiation, donor support and implementation of the community-based nutrition programmes. The economic aspects of nutrition recovery were mainly a reflection of social the supply of essential goods and commodities including drugs, farm inputs, oil (cooking, kerosene, vehicle oils), clothes etc which were unavailable during the severe crisis years of the early 1980s. It is not an exaggeration to say that it was also a reflection of the hopeful situation created by the availability of these essential goods. However, we would like to stress that the nutrition improvement was a direct result of the implementation of the nutrition programmes, as it has been observed that the improvement only occurred in areas where nutrition improvement was explicitly made a goal to be achieved.

Table 12: The rates of reduction of malnutrition in CSD regions (1984-1991)

Region

Prevalence rate at baseline

Prevalence rate 1991

Programme period (yrs)

Rate of reduction (%)

Iringa:

(1984)





Severe

6.3

1.3


79.4

Moderate

49.5

33.9

7

31.5

Total

55.8

35.2


36.9

Kagera

(1985)





Severe

7.9

2.2


72.2

Moderate

49.0

33.7

6

31.2

Total

56.9

35.9


36.9

Kilimanjaro

(1987)





Severe

3.5

0.3


91.4

Moderate

30.7

11.9

4

61.2

Total

34.2

12.2


64.3

Mara

(1990)





Severe

7.4

1.9


74.3

Moderate

30.7

25.9

2

15.6

Total

38.1

27.9


26.8

Morogoro

(1988)





Severe

4.3

2.9


32.6

Moderate

40.2

36.5

3

9.2

Total

44.6

39.4


11.7

Mtwara

(1987)





Severe

8.0

3.2


60.0

Moderate

46.9

38.0

4

19.0

Total

54.9

41.2


25.0

Ruvuma

(1987)





Severe

4.9

5.0


(-2.0)

Moderate

49.7

33.2

4

33.2

Total

54.6

38.2


30.0

Singida

(1990)





Severe

2.7

1.7


37.0

Moderate

32.9

31.7

1

3.6

Total

35.6

33.4


6.2

Shinyanga

(1988)





Severe

3.4

1.3


61.8

Moderate

38.5

25.8

3

33.0

Total

41.9

27.1


35.3

All regions (except Singida)






Severe

5.6

2.3


58.9

Moderate

41.9

29.9

4.1

28.6

Total

47.5

32.2


32.2

Source: Calculated from CSD Data base (1992)

Nutritional status of school age children

Information regarding the nutritional status of school children is very limited. The information available however, indicates that PEM also affects school age children, but to a lesser extent than the under-fives.

In a survey of primary school children aged 8-18 years in eight regions in mainland Tanzania (Kimati and Scrimshaw 1985), found a considerable prevalence of PEM but lower than for under-fives. The boys were slightly more affected than girls. Table 13 indicates the prevalence of malnutrition in children aged 7-13 years in Mbeya and Rukwa regions in 1987.

Table 13: The prevalence of under-nutrition in children aged 7-13 years in Rukwa and Mbeya regions in 1987

Age (years)

Sample size

Percent below median -2SD

Weight-for-age

Height-for-age

Weight-for-age

7

105

11

5

0

8

149

8

10

4

9

260

18

28

4

10

437

11

44

3

11

451

16

52

3

12

449

30

62

1

13

263

37

71

1

7 - 13

2,114

19

39

2

Source: TFNC, 1987
An important observation from table 13 is that stunting progressively increases with age reaching its peak of about 70 percent around puberty when the adolescent growth spurt may probably slow down the trend. Since genetical differentiation starts after five years the high prevalence of stunting based on the NCHS reference values may in part be a reflection of genetical differences rather than chronic under-nutrition as such.

Nutritional status of adolescents

This is a grey area in Tanzania as there are only a few studies which have looked at the nutritional status of adolescents. These studies corroborate observations that though much less affected by the acute forms of malnutrition than children; the prevalence of stunting is very high reaching about 70 percent at 13 years (table 13 above).

Stunting in adolescent girls is of particular concern and constitutes a major impediment to safe motherhood. Stunting in girls is associated with a contracted pelvis which can lead to obstructive complications during delivery and may cause maternal or perinatal deaths. The problem of contracted pelvises is made worse by early pregnancies where teenage girls become pregnant even before they have attained full skeletal development. At menarche young women still have approximately 4 percent more height and 12-18 percent more pelvic growth ahead of them. Even after 2 years of menstruation an additional pelvic growth of 3-9 percent and height growth of 1 percent may be achieved. This indicates the possibility of improving the nutritional status of the adolescent girl by taking advantage of the growth spurt in order to improve maternal nutrition and mortality. It is an area where research could make great contribution.

Nutritional status of adults

In Tanzania, where the population is young and depends on a small adult population to sustain the economy and to provide the resources needed for adequate food, care and health; malnutrition in adults is of serious consequence. In such a situation, malnutrition becomes both a result and a cause of poverty. Malnourished adults cannot respond well to the challenges of economic and even political reforms which need both physical and mental energy. Maternal malnutrition is of even more severe consequence, as malnourished women produce malnourished children who will grow into malnourished adults creating a vicious cycle.

The nutritional status of adults is anthropometrically measured by using the Body Mass Index (BMI) which is a number derived from dividing the subject’s weight in kilograms by the square of the height in metres (W/H2). For purposes of this review the following cut-off points for BMI have been used for both men and women:- less than 20 is undesirable or undernourished; 20-24 is desirable or well nourished; 25-29 is overweight; and 30 or more is obesity. Under-nutrition with a BMI of less than 20 indicates chronic energy deficiency and could further be sub-divided as follows: (a) Mild deficiency (BMI of 18.5 - 19.9) (b) Moderate deficiency (BMI of 16 - 18.4) and (c) severe deficiency (BMI of less than 16). The minimum BMI below which death occurs is about 14. Pregnancy shifts the BMI to the next higher index and may rise up to 4 units if the required weight gain of 12 kg is achieved in a pregnancy.

The distribution of Body Mass Index (BMI) in a few regions surprisingly show that more than a third of adults have low BMIs with rural areas having higher prevalence rates of undesirable BMIs than urban areas table 14.

It can be observed from the table that males are lighter than females. Since theoretically the body size of females should usually be smaller than that of males, and the cut-off of undesirable BMI for females is recommended to be 18.5 it would be expected to see a higher prevalence of a BMI of less than 20 in females than in males. A possible explanation could be that the sample of females consisted mainly of women who had already borne children. It would be very useful if future studies would specifically look at the adolescent girl in both rural and urban settings to know better the nutrition state at which women start their reproductive life. However it should be noted that the high prevalence of low BMI points out to the need to revisit the impression held by many people that malnutrition is not a problem in adults particularly male adults.

Maternal malnutrition

Maternal malnutrition presents a different spectrum of problems. Apart from limiting their productive capacity, maternal malnutrition also makes their reproductive role to be unsafe, sometimes leading to the death of the new born, the mother or both. In some cases complications in the birth canal in the form of fistulas may lead to permanent reproductive disability with all the attendant social stresses. The problem of maternal malnutrition may start in early childhood as shown by the pattern of child growth in Tanzania.

Available information shows that generally children grow normally up to the age of six months presumably due to the universal breast-feeding which is about 40 percent exclusive up to that age. Thereafter, growth retardation starts to appear, with subsequent increases in the rates of malnutrition. The commonest type of malnutrition during this early ages is wasting (low weight-for-height), an acute type of malnutrition whose prevalence increases to a peak during the second year when diarrhoea also peaks. This is related to the onset of diseases due to the waning of maternal passive immunisation and improper weaning. It seems that survival of the acute forms of malnutrition leads to adaptation in the form of stunting (low height-for-age) which is a chronic form of malnutrition. Stunting decreases the physiological nutrition requirements, so that in their stunted condition the children will seem to meet their nutritional requirements. Stunting increases with age as children accumulate height deficits due to repeated episodes of disease, accompanied by poor weaning practices making stunting the commonest form of malnutrition overall. This growth pattern is similar for boys and girls making the prevalence rates of malnutrition to be equal for the sexes. Apart from the physical and mental functional limitations brought about by malnutrition for girls it is a major cause for poor reproductive performance in adulthood leading to unsafe motherhood. The scientific explanation to this is related to the phenomenon of catch up growth.

Table 14: Percent (%) distribution of BMI by age in five regions in Tanzania.

Sex/Region

No.

Mean age in years

Percent with BMI of:-

< 20

20-24

25-30

> 30

Males

Dar-Es-Salaam

627

-

34.6

50.8

12.6

2.1

Kilimanjaro

1,143

-

45.2

51.6

3.0

0.1

Morogoro

1,452

-

43.7

53.9

2.3

0.1

Mara

478

-

44.3

51.6

3.1

0.9

Arusha

25

-

32.0

44.0

24.0

0.0

Mean

3,725

-

40.0

50.4

9.0

0.6

Females

Dar-Es-Salaam

830

-

22.8

47.8

19.5

10.0

Kilimanjaro

1,865

-

36.3

53.5

8.8

1.4

Morogoro

1,224

-

37.6

54.0

7.3

1.1

Mara

584

-

36.2

55.9

7.1

0.8

Arusha

47

-

36.2

48.9

14.9

0.0

Mean

4,550

-

33.8

48.9

11.5

2.7

Both sexes

Dar-Es-Salaam

1,457

34

28.7

49.6

16.1

6.1

Kilimanjaro

3,008

38

40.7

52.6

5.9

0.8

Morogoro

3,089

35

40.6

53.9

4.8

0.6

Mara

1,062

34

40.2

53.7

5.1

0.8

Arusha

72

-

34.7

47.0

18.1

0.0

Mean

8,688

35

36.9

51.4

10.0

1.7

Source: Swai A.B et al, 1989 and Kisanga 1987
While catch growth in weight for height is attained; for the majority catch up growth in height is not attained due to the slowness with which catch up growth in height takes place. Coupled with the fact that in most cases adverse nutritional factors continue even after five years of age the result is that stunting continues well into adolescence and adulthood making people in Tanzania to be generally shorter than their genetical potential would allow. Stunting in women which leads to obstructive complications during delivery is the major reason for surgical intervention and is thus directly or indirectly related to wound infection which is one of the major causes of maternal deaths in Tanzania. The average height of women in Tanzania is 156 cm which is very close to the at risk cut-off level of 150 cm. The DHS data on women’s stature covering 5,238 women showed a mean height of 155.9 cm similar to the 156 cm found in the Ilula study (URT/Bureau of Statistics, 1993; Moller et al 1988). In the DHS data, 3.7 percent of the women had a height below 145 cm, and for 16.7 percent it was below 150 cm.

Maternal malnutrition is also indicated by low maternal Body Mass Index (BMI), low weight gain during pregnancy, low nutrient intake and a high prevalence of low birth weight.

By using the cut-off point of a BMI of less than 20 as indicative of under-nutrition; some cross sectional studies conducted in Ilula village in Iringa region in 1985 showed that a third of non-pregnant and lactating mothers have undesirable BMI [Kavishe et al 1987] (table 15). The prevalence of an undesirable BMI of 33.3 percent compares well with the average of 33.8 percent found in the five regions presented earlier (table 14).

Table 15a: The nutritional status of women according to their reproductive stage in Ilula village Iringa region

Reproductive stage

no.

Percent with a BMI of:-

< 20

20-24

25-29

30 and more

Total

1. Non-pregnant

45

33.3

62.2

4.4

0.0

100

2. Pregnant

77

13.0

62.3

20.8

3.9

100

3. Lactating

294

33.2

60.7

5.4

0.7

100

Total/Mean

416

29.5

61.2

8.1

1.2

100

Source: Kavishe et al (1987)
The DHS data on the stature and BMI of women shows that while on average there is no difference in the stature of rural and urban women, urban women are heavier as indicated by a higher average BMI (table 15b). On average women in Zanzibar are shorter and lighter than women in Tanzania mainland. If mild under-nutrition (BMI 18.5-19.9) is included the DHS data shows a national prevalence of BMI of less than 20 to be 27.8 percent which is very close to the Ilula study (table 15a). Taking the rural prevalence of BMI of less than 20 alone it is clearly similar to the 29.5 percent found in non-pregnant women in Ilula.

Table 15b: The stature and BMI of women in Tanzania, 1991/92

Area

Stature (cm)

Body Mass Index (BMI)

n

Mean (cm)

Percent less than 145

n

Mean

Percent less than 18.5

Mainland

5,093

155.9

3.7

4,185

21.7

9.6

Dar-Es-Salaam

272

154.3

4.6

230

22.8

5.8

Other urban

899

155.2

4.5

765

22.4

6.3

Rural

3,921

156.2

3.4

3,189

21.5

10.7

Zanzibar

145

154.8

6.3

120

21.3

11.5

Tanzania

5,238

155.9

3.8

4,305

21.7

9.7

Source: URT/Bureau of statistics, TDHS 1993
Both maternal weight and weight gain during pregnancy is low. The mean maternal weight in rural areas as exemplified by one study of 331 women is about 53 kg (Moller et al 1988) as compared to a recommended weight of 55 kg. The average gain of weight during pregnancy is about 6 kg compared to a recommended gain of 12 kg. On average there is no net weight gain during pregnancy and up to 12 percent of women do not gain or even loose weight. The major reason for this is the low maternal intake and heavy workload (high energy expenditure) during pregnancy.

An examination of dietary intake and energy expenditure during pregnancy indicate that generally women eat to meet only two thirds of their energy needs, and their intake roughly matches their energy expenditure [Kavishe et al 1987]. Table 16 illustrates the mean maternal dietary adequacy during pregnancy in the Ilula study. While the average energy intake during the third trimester averaged 1,595 Kcal. per person per day; the energy expenditure in a sample of 18 women was as high as 1,500 Kcal. in 77 percent of them, indicating that the heavy workload of women continues well into the last days of pregnancy. This excludes the energy cost of the pregnancy itself.

Table 16: The mean maternal dietary adequacy during pregnancy in Ilula village, Tanzania, using weighing and record method

Type of nutrient

Percent adequacy in various gestational periods

12-16 weeks (n=6)

22-24 weeks (n=24)

32-36 weeks (n=18)

Total: 12-36 weeks (n=48)

1. Energy (RDA 2400)

63.7

67.9

67.8

66.5

2. Protein (RDA 52g)

90.0

100.0

110.0

100.0

Source: Kavishe F.P et al (1987)
Low birth weight

Low maternal weight gain during pregnancy, low dietary intake, and heavy workload conspire leading to the birth of low weight babies.

Table 17: Prevalence of hospital based Low birth Weight in Tanzania Mainland 1985-1991

Year

Number of live births or sample size

Mean birth weight (gms)

Percent (%) weighing less than 2,500 g

1985 (estimates)

n.a

3,100

14.0

1986 (DHS of 1991/92)

8,032

3,024

16.9

1986/87 (19 regions)

3,350

2,900

10.0

1990

497,930

-

8.4

1991

549,771

-

8.7

Source: TFNC report no. 1322; TDHS, 1993 and MCH returns 1990-91
Thus low birth weight (LBW) defined as a birth weight of less than 2.5 kg is an important indicator of the wellbeing of neonates and women of reproductive age. In addition birth weight is an important indicator of the survival of the newborn.

The trend of LBW in Tanzania shows some improvement (table 17). There are wide regional differences in the prevalence rate of low birth weight which also implies regional differences in maternal nutrition and survival of the newborn (table 18). The range for 1991 varied from 4.0 percent in Dar-Es-Salaam region to 21.0 in the Pwani (Coast) region. The 1991-92 DHS gave national estimates of the mean birth weight and prevalence of low birth weight in the five years preceding the survey which meant 1986/87. It also showed that about 53 percent of deliveries in mainland Tanzania take place in a health facility, indicating that there is still a substantial number of women who deliver at home and whose newborns may not be reflected in the rates of LBW. The apparently declining trend of the prevalence of LBW means that Tanzania can achieve the World Summit goal of a LBW prevalence of less than 10% by the years 2,000.

Table 18: Regional prevalence of low birth weight (1990-91), Tanzania Mainland

Region

1990

1991

Sample

% LBW

Sample

% LBW

Arusha

27,065

6.1

28,377

6.1

Dar-Es-Salaam

51,597

10.0

52,761

4.0

Dodoma

23,749

8.0

23,495

8.0

Iringa

25,709

12.3

29,639

11.0

Kagera

20,918

9.6

21,933

10.0

Kigoma

23,378

11.0

24,716

8.0

Kilimanjaro

23,521

7.8

16,304

12.1

Lindi

15,646

9.2

16,586

7.7

Mara

12,949

8.0

16,522

7.0

Mbeya

32,228

5.1

37,863

4.5

Morogoro

27,803

9.0

45,043

8.0

Mtwara

22,155

9.0

24,693

13.0

Mwanza

35,908

10.5

40,287

9.0

Pwani

14,393

9.0

17,687

21.0

Rukwa

13,871

6.2

15,082

6.7

Ruvuma

20,978

8.5

26,152

9.9

Singida

14,268

6.0

16,099

5.0

Shinyanga

35,599

8.0

34,728

10.0

Tabora

28,473

10.0

33,238

8.0

Tanga

27,722

5.5

28,566

5.7

Average

497,930

8.4

549,771

8.7

Source: MCH returns 1990-91

Micronutrient Malnutrition


Iodine deficiency disorders (IDD)
Vitamin A deficiency (VAD)
Nutritional Anaemia
Fluorosis

The micronutrient deficiencies of public health significance are iron/folate, leading to nutritional anaemia; iodine leading to iodine deficiency disorders (IDD) and vitamin A leading to xerophthalmia and nutritional blindness. Other micronutrient deficiencies like of vitamins C (causing scurvy) and B (causing pellagra) are known to occur but are of little public health significance. Deficiencies of zinc and selenium which have been found of public health significance in some areas of India and China have not been noted in Tanzania, but their presence may become apparent as the overriding problems of PEM and the major three micronutrients deficiencies are successfully addressed.

As for Protein Energy Deficiency (PED) micronutrient malnutrition affect mainly under-five year children and pregnant and lactating women. The large populations affected and the wide spread spatial distribution of the micronutrient deficiency problems is among the important factors responsible for the urgent development of national micronutrient deficiency control programmes.

Iodine deficiency disorders (IDD)

Available data on IDD is based on an ongoing national survey started in 1980 and coordinated by TFNC where a random sample of 10 primary schools per district were surveyed as regards goitre prevalence.

On the basis of these goitre surveys TFNC estimates that nearly 40 percent of the Tanzanian population or 10 million people live in areas deficient of iodine and are, therefore, at risk of IDD. It is further estimated that out of those at risk, 5.0 million suffer from endemic goitre, 160,000 are cretins and probably 450,000 are cretinoids. This means that about 5.61 million people or nearly 25 percent of the total population suffer from IDD [van der Haar, Kavishe and Gebre-Mehdin 1988]. Furthermore it is estimated that about 30 percent of the perinatal mortality may be attributable to iodine deficiency. These estimations were done before the start of the capsule supplementation program in 1986, and evaluation results indicate that the problem of IDD may have substantially decreased over the past three to four years [Kavishe 1991].

Iodine deficiency disorders (IDD) show a true geographical pattern. Since the basic cause of IDD is iodine deficiency in the soil it is expected that crops grown or animals reared in such soils will also be iodine deficient. Thus IDD is expected to exhibit a geographical pattern on the basis of geophysical features favouring iodine loss from the soil like heavy rainfall highlands and low lands subjected to frequent floods. In fact this seems to be the case in Tanzania and worldwide; and IDD remain one of the most illustrative major public health problems in Geographical medicine. In Tanzania, the severity of IDD is highest in high altitude volcanic soils and a distinct pattern following the highlands and mountains of Western and Eastern arms of the Great Rift valley as it descends from the Kenya border to their confluence in lake Nyasa in the South can be discerned in the form of an “IDD belt”.

Unfortunately these are also the most fertile and climatically most favourable areas leading to a high concentration of the population. The severely IDD affected areas are also the most agriculturally productive and export food to the iodine sufficient areas. In such a situation, it cannot be expected for IDD to be controlled through trading of food. Map 2 indicates the severity and geographical distribution of the problem.

Map 2: Geographical distribution of goitre in Tanzania on basis of surveys, 1980 - 1990

Vitamin A deficiency (VAD)

The situation with regard to the problem of vitamin A deficiency in Tanzania is based on the results of a hospital based sentinel xerophthalmia surveillance system started in 1982 (Foster, Kavishe, and Sommer et al 1986) and various community based surveys [Pepping, Kavishe and Hacknetz et al, 1988].

The findings were suggestive of a problem of xerophthalmia of public health significance in the wider community at least in restricted areas and in at risk groups (table 19). Within areas, remarkable differences can be found between villages within an area. This clustering may be either in certain villages or parts of those villages and it is possible that it may extend to certain agro-climatic zones of the country.

Primary vitamin A deficiency and xerophthalmia though uncommon, mostly occurs in the drought prone semi-arid regions like Dodoma, Shinyanga and parts of Tabora and Mwanza. Since xerophthalmia is mainly associated with measles the geographical differentiation is expected to be more pronounced as the control of measles is been achieved. Because the major sources of vitamin A are the seasonal vegetables and yellow fruits; subclinical vitamin A deficiency is likely to follow the seasonal variations. Better methods for the drying of vitamin A rich vegetables for use during the dry season in the drought prone areas are needed as the present practice of sun dying destroys most of the vitamin A. As seen from table 19, the prevalence rates for serum retinol below 20 ug/dl are very closely correlated with that of abnormal conjunctival impression cytology (CIC). The rates of xerophthalmia, low serum retinol levels and abnormal CIC are typical of areas with a problem of vitamin A deficiency of public health significance.

On the basis of information available, TFNC has estimated that xerophthalmia leads to between two and four thousand new cases of blindness every year so that at any one time about 10,000 children are likely to be suffering from nutritional blindness at any one time [TFNC, 1990]. It is further estimated that vitamin A deficiency and xerophthalmia affects about 1.36 million people or 6 percent of the Tanzania population among whom 1.33 million or 98 percent of those affected are children under 6 years of age.

Table 19: Results of hospital surveillance (0-10 years) and community surveys on vitamin A deficiency in children under six years Tanzania (1982-1992)

Type of survey


Sample size

Prevalence criteria

Prevalence (%)

1.

Hospital Surveillance

(1982-83)

10,498

Corneal scar

1.4

(1983-84)

10,363

Corneal scar

1.3

2.

Community surveys





Various

(1983-1985)

15,233

Corneal scar

0.3

Iringa

(1984, Mar-Jun)

5,975

Corneal scar

0.4

(1984, Nov)

1,331

Corneal scar

2.7

Tabora

(1985, Mar)

556

Bitot’s spot

2.1

(1986, Feb)

3,177

Bitot’s spot

6.0

Shinyanga

(1988, Sep-Oct)

3,518

Corneal scar

0.2



Retinol


Singida

(1991, Oct-Nov)

226

< 10 ug/dl

14.6



< 20 ug/dl

60.2


89

Abnormal CIC

52.8

(1991, Nov-Dec)


Retinol



250

< 10 ug/dl

3.0



< 20 ug/dl

35.4


275

Abnormal CIC

35.6

Kigoma

(1992, Feb-Mar)

112

Retinol




< 10 ug/dl

6.5



< 20 ug/dl

43.4

Singida

(1992)

238

Retinol




< 10 ug/dl

1.3



> 20 ug/dl

26.9



Night blind

0.4



Xerophthalmia

2.5



Abnormal CIC

32.9

Key: CIC = Conjunctival Impression Cytology

Source: Pepping et al, 1988 and TFNC, 1992

Nutritional Anaemia

Due to lack of wide spread objective diagnostic facilities, the epidemiological picture regarding the nature and extent of anaemia in Tanzania is incomplete. A recent study commissioned by TFNC [Mnyika 1991] reviewed all information related to anaemia and gathered data from 15 hospitals in 14 regions in mainland Tanzania based on the four geo-climatic zones (Coastal, Plateau, Lake, and Highland zones). The information collected indicated that among admitted under-five years, anaemia accounted for 20 to 80 percent of the admissions, while in pregnant women it accounted for 18 to 87 percent. Also anaemia was a direct cause of 5 percent of maternal mortality and an underlying cause in 63 - 73 percent of maternal deaths. These estimates are gross underestimates of the problem because the cut-off point for anaemia used in the rural hospitals and clinics is a haemoglobin (Hb) of < 8.5 g/dl which has been arbitrary chosen as the level at which a pharmaceutical approach is taken.

The WHO definition of anaemia at sea level [WHO, 1986] is a Hb of 11 g/dl for children aged 6 months to 6 years and pregnant female adults; 12 g/dl for older children up to 14 years and no-pregnant adult females; and 13 g/dl for adult males. The cut-off point used by TFNC is a Hb of 10 g/dl irrespective of sex and age [Kavishe, 1982] as this is the level at which there is no stainable iron in the bone marrow and symptoms and clinical pallor start to be noticed. The use of a haemoglobin cut-off level of less than 8.5 g/dl by the Ministry of Health for diagnosis of anaemia in the Ante-Natal Clinic was arbitrarily chosen as the trigger level for individual treatment and was based on the extensiveness of the problem and the scarce resources available for intervention. As a result a large section of the population who would be defined as anaemic according to WHO and TFNC reference values are left out of the anaemia equation.

An examination of available community based information collected by TFNC [Kavishe, 1991] shows that the prevalence of anaemia varies from 0 to 100 percent depending on population groups considered and the geographical location. Pregnant and lactating women and children under-five years constitute the most vulnerable groups. Multiparous women are more severely affected than primigravidae [Mnyika, 1991]. In areas where hookworm and schistosomiasis are highly prevalent, school children and even adult males are affected equally. A recent community-based study in four regions (Kilimanjaro, Dar-Es-Salaam, Morogoro and Mara) in a sex matched sample of 8616 subjects aged 15 years and over, revealed that the prevalence of anaemia was 21 percent without a gender difference [Kitange et al, 1991].

Where the anaemia has been typed it has been found that it is mainly of iron deficiency in nature with other factors usually worsen the situation. At Muhimbili Medical Centre during the late 1970’s anaemia in pregnancy constituted about 50 percent of antenatal admissions in obstetrics and gynaecological wards and of these 83 percent were due to iron deficiency while 43 percent were due to folic acid deficiency [Mnyika, 1991]. At the same hospital in 1982 about 80 percent of paediatric anaemia were of nutritional origin, only 20 percent being due to other causes. In pregnancy and anaemia of malarial origin, mixed iron and folate deficiencies are also found. Even today nutritional anaemia remains the number one cause of obstetric admissions and maternal mortality in Tanzania. National estimates of the extent of the problem of anaemia indicate that on a general population basis nutritional anaemia is the most wide spread nutritional disorder in Tanzania, affecting 7.2 million people or 32 percent of the population with 45 percent of children under-five and 80 percent of pregnant and lactating women affected [Kavishe, 1987].

There is also a geographical distribution of anaemia which is determined by altitude and disease patterns. The problem is most serious in the coastal belt and other low altitude areas and decrease to negligible levels in altitudes above 3000 metres. This pattern mirrors that of malaria. The geographical pattern is associated with other factors such as sickle cell disease, the low bio-availability of iron from the mainly vegetable based sources; the effects of parasites especially malaria, intestinal parasites and bilharzia; and the widespread protein energy under-nutrition. Control of these diseases, and improved diet by use of more fruits and vegetables rich in iron and vitamin C would improve the problem. Germination and fermentation of foods using traditional methods also increases the bio-availability of iron. Additional iron and folate supplementation through the Maternal and Child Health (MCH) and Essential Drug Programme is needed to supplement pregnant women since diet alone cannot rectify deficiencies quickly enough. Regions with high fertility levels like Kilimanjaro, Kagera and Rukwa also seem to have higher prevalence rates of anaemia than would be expected. This is because of the frequent pregnancies which deplete the maternal iron reserves.

Fluorosis

Since fluorosis is due to an excess of fluoride in the soil and thus in the water and foods originating from such soils; a geophysical pattern is again evident with the northern and north western regions of Kilimanjaro, Arusha, Singida and Shinyanga being most affected. Most studies of fluorosis have been done in Arusha. The most severe focus with skeletal deformities is the Kitefu village in Arusha [Kisanga 1987]. At present the only approach taken to control the problem is to look for sources of water relatively low in fluoride when developing water systems in fluorosis affected areas.

Diet and nutrition related non-communicable diseases

Tanzania has started to enter the transition from the dominance of infectious diseases to that of non-infectious disease. This trend is particularly obvious in the urban areas where rapid urbanization has resulted in a shift away from the traditional cereal-based diet and home produced foods to the more high fat; energy-rich; high salt and processed sugar-based foods.

Processed foods are now readily available following trade liberalization. Sugar based soft drinks; alcohol and ready to eat food items like fried chips, eggs, roasted meat, sweets, chocolates etc have sprung up in street kiosks in most towns in the country. In addition the more elite and business sections of urban communities have emulated sedentary unhealthy life styles which add to the risk of developing diet related non-communicable diseases.

This has resulted in three distinct population groups as far as nutrition is concerned. The first and major group is the rural population who have maintained their traditional diets which are often energy deficient and have continued to suffer from under-nutrition related problems. The second group is the urban middle and low income urban slum dwellers who despite their low purchasing power insist on consuming animal and refined foods rather than the more affordable staple cereals and tubers. This limits their access to food and often results in serious dietary and nutritional inadequacies. The third group is the urban elite and business group who have drastically altered their food habits and tend to consume high fat, high-salt, energy-rich and sugar based food items. They also lack physical exercises. This is the group which has been most affected by the transition and tend to suffer from diet related non-communicable diseases at rates similar to those in the industrialized countries.

Thus the urban population in Tanzania is confronted with the dual problems of under-nutrition and over-nutrition. While the former problem increases the risk of communicable diseases; the latter increases the risk of occurrence of chronic non-communicable diseases like coronary heart disease, hypertension, adult onset diabetes, dental carries etc.

Some observers have described three stages in the evolution of health care in the industrialized countries. Stage one was the time when communicable disease and malnutrition was common. As communicable disease diminished through improved nutrition, better health care, education, water and environmental sanitation, housing, and general social-economic development; stage two emerged as the stage of non-communicable diseases mainly of the cardiovascular and cerebrovascular systems. There is evidence that there is a decline in the stage two diseases in the industrialized countries and that a third stage of environmental and social pathology has started to emerge. The major threats in stage three are health problems from environmental exposure and changes in the social conditions in the family, community and work place. In Tanzania and other developing countries the present medical scene seem to consist of all three stages at varying degrees of severity. This presents a challenging dilemma for planners and decision makers.

At the present time nutritional programmes largely and rightly so focus on the problems of stage one in the most affected groups; children and women. At the same time Tanzania has taken up the challenge in addressing the problems of stage two and is participating in the WHO Inter-health Programme which seeks to prevent and control risk factors which are common to several of the major non-communicable diseases like smoking, excessive alcohol intake, obesity, lack of exercise etc. Apart from Mauritius, Tanzania is the only continental African country participating in this programme [Alberti, 1991].

An important question to ask at this stage is whether there is evidence for the existence of a problem of diet related no-communicable disease in Tanzania which merits some of the actions taken so far.

In answering this question some few facts have to be borne in mind. The first is that the causes of morbidity and mortality in adults who constitute the major population group affected with no-communicable diseases is not well known particularly at the community level. The second is that only very few studies have been done to look at the problem. Table 20 provide some insight into the causes of death in communities in Morogoro region.

Table 20: Causes of 97 adult deaths in Morogoro region (1990)

Cause of death

Percent

1.

Infection

40

2.

Nutritional Anaemia

15

3.

Circulatory disorders

11

5.

Accidents

6

6.

Surgical emergencies

6

7.

Miscellaneous

6

8.

Pregnancy related

4

9.

CNS disease/Epilepsy

4

10.

Diabetes

4

11.

Liver disease

3

12.

Congenital problem

2

13.

Asthma

1

14.

Neoplasm

1

All Causes

100

Source: Non-Communicable disease project MMC (1990)
Some recent community studies have shown that the problem of diet related non-communicable disease and its associated factors is considerable as shown in table 21 below. The studies were conducted in Dar-Es-Salaam (n = 1457), Kilimanjaro (n= 3008), Morogoro (3089) and Mara (1062) regions.

Table 21: The prevalence of diet related non-communicable diseases in three regions of Tanzania

Disease

Prevalence

1. Hypertension

3.0 - 12.8

2. Diabetes mellitus

0.5 - 1.1

3. Obesity (BMI > 30)

0.1 - 10.8

4. Smoking

1.3 - 42.0

5. Alcohol consumption

4.1 - 78.0

6. High blood cholesterol

0.6 - 7.8

7. High blood triglycerides

7.9 - 13.2

Source: Swai and McLarty et al 1990
A regional desegregation of the data shows that with the exception of cholesterol levels, Dar-es-Salaam has the highest prevalence of risk factors as shown in table 22. The high prevalence of high blood cholesterol levels in Kilimanjaro region may be a reflection of the custom of eating roasted meat.

Table 22: The prevalence (percent) of the major risk factors associated with diet related disease in three regions of Tanzania

Region

n

BMI > 30

Hypertension (more than 90/140 mmHg)

High Blood values of

Cholesterol
(> 6.5 mmol/l)

Triglycerides
(> 1.7 mmol/l)

1. Dar-Es-Salaam

1,457

6.1

12.8

3.4

13.2

2. Kilimanjaro

3,008

0.8

6.8

7.8

10.3

3. Morogoro

3,089

0.6

4.0

0.6

?

4. Mara

1,062

0.9

3.0

0.7

?

Average

8,616

2.1

6.9

3.1

?

Source: Swai A.B and McLarty et al 1990
It has already been indicated in the section on adult malnutrition that the tendency to overweight and obesity is much higher in women than in men. However, men are affected by the risk factors at lower levels than women.

Apart from obesity the prevalence of cardiovascular risk factors is the same in the urban areas irrespective of occupation as shown in table 23.

The conclusion is that cardiovascular risk factors related to nutrition especially of serum cholesterol levels make it unlikely for diet related Cardiovascular Heart Disease (CHD) to emerge as a significant health problem among the rural population in the near future, but for the urban population the risk is real.

Table 23: The prevalence of risk factors according to employed occupation

Risk Factor

Prevalence of risk factor (percent)

Executives (n= 138)

Non-executives (n= 77)

1. Mean age (years)

47 (SD 7.2)

41 (SD 8.8)

2. Diabetes

9.7

2.6

3. IGT

12.7

9.2

4. Hypertension

30.4

39.0

5. Obesity

29.0

11.7

6. Smoking

13.0

19.0

7. Alcohol drinking

45.0

68.0

Source: Lwakatare et al 1990
Most efforts should at the present time be concentrated on the prevention and control of hypertension and its complications and to emphasize a healthy lifestyle which should include a fair amount of exercise, healthy eating habits and the benefits of not smoking. For Tanzania to escape the problems of health care evolution described for the industrialized countries, a balanced approach to the problems of under-nutrition and over-nutrition will need to be pursued. Seychelles and Mauritius should serve as examples of the real risk of how the balance can be tipped to problems of over-nutrition.

Malnutrition and the Mortality pattern


Maternal mortality
Perinatal mortality
Infant and Under-five year child mortality

The level of young child and maternal deaths and general malnutrition rates are usually regarded as the ultimate manifestation of the nutrition situation in a country. The nutrition situation directly affects the life expectancy of a community, and therefore, their quality of life. The pattern of young child and maternal mortality rates should, therefore, be considered in the discussion on malnutrition. Since data on mortality are not reliably nor comprehensively reported, the trends and patterns presented are based on data from health facilities and from a few community surveys.

Maternal mortality

Maternal mortality and morbidity constitute one of the major health problems in Tanzania. A maternal death is defined as the death of a woman while pregnant or within 42 days of the termination of pregnancy.

An examination of the institutional trend for maternal mortality in Tanzania indicate a rapid substantial decline, from about 450 per 100,000 live births in 1961 to about 200 in 1974, and since then it seems to have /stabilized around 200 maternal deaths per 100,000 live births with a tendency to fluctuate upwards. During the two years of 1990 and 1991, the fluctuations showed a definite increasing trend, reflecting a deterioration in the quality of health services which have been a major victim of both the economic crisis and structural adjustment. Maternal mortality is a measure of the quality of health services.

Table 24 shows the trend of maternal mortality from 1961 to 1991. It should be observed that although both the average national and regional figures (table 25) are below the 640/100,000 live births estimated for Africa, the deteriorating trend is a major reason for alarm. Moreover, the rate in Tanzania is over 60 times the rate in countries in the North. Compared to women in the North, Tanzanian women are 200 times more likely to die in childbirth because of the high risks during individual births combined with the higher number of children borne. This differential risk of death is greater than for any other population group. The risk is also greater for women in the southern and western regions of the country than elsewhere.

Table 24: The trend of maternal mortality in Tanzania Mainland 1961-1991

Year

Maternal mortality per 100,000 births

1961

453

1967

351

1972

252

1985

167

1986

197

1987

190

1990

190

1991

215

Source: Mandara and Kaisi (1991) and MOH, Family Planning Programme (1992)
A regional desegregation of the information shows wide regional variations and trends and alarmingly high levels of maternal mortality as shown in table 25.

The only region with an average maternal mortality rate below 100/100,000 live births is Kilimanjaro which also shows the best trend in reducing maternal mortality. Other regions where there is an overall reduction are Kagera, Lindi, Morogoro, Ruvuma and Tabora. The remaining regions show an overall increasing trend, the worst regions being Dar es salaam, Dodoma, Iringa, Mbeya, Mara, Singida and Tanga. In 1991 the maternal mortality ranged from a low 43/100,000 live births in Kilimanjaro region to a high 455/100,000 live births in Iringa.

Regions with rates above 400 are Iringa and Mbeya. Only Singida is between 300-400. Those between 200-300 are Tanga, Singida, Mwanza, Mara, Lindi, Kagera and Dodoma. The worsening situation in Iringa, Mara and Singida is paradoxical when considered in the context of the very successful Joint Nutrition Support Programme (JNSP) in Iringa and the subsequent Child Survival and Development (CSD) programmes in Iringa and the other regions. The explanation is that overall, the situation of women in the CSD programmes was not given adequate programmatic action to the extent given to child mortality and malnutrition. There is need to further analyze the alarming situation of maternal mortality taking into account the results of the recent Demographic and Health Survey (DHS) so that positive lessons can be drawn in reversing the situation.

The immediate causes of maternal deaths recorded in hospitals are haemorrhage, sepsis, obstructed labour, anaemia and malaria. Though the majority of the direct causes are due to direct obstetric causes (fig 4); most of the risk factors to those causes are related to nutrition. The high prevalence of chronic malnutrition in childhood (see section on children) leads to stunting, with the result that a large proportion of adult women are short (under 145 cm), with narrow pelvises which may obstruct labour. Maternal height is both a gauge of past nutritional state and present size, with all genetical provisos recognized. It correlates well with pelvic bony growth in earlier life and thus the possibility of obstructed labour in short women. Moreover over one-third of women bear children at too early an age (less than 20 years) before they are physically mature increasing the risk of obstructed labour. The DHS data of 1991-92 show that teenage pregnancies are very common, 23.2 percent for Tanzanian Mainland and 23.9 percent for Zanzibar with the respective age at first birth being 18.8 and 18.4 years. The overall median age at first intercourse which is almost 100 percent unprotected, was found to be 16.6 years.

Table 25: Regional variations in maternal mortality in Tanzania 1985-1991

Region

Maternal Mortality per 100,000 live births

1985

1986

1987

1990

1991

Arusha

117

205

142

110

134

Dar es salaam

46

43

44

100

114

Dodoma

127

137

53

162

215

Iringa

192

147

258

306

455

Kagera

443

315

223

271

204

Kigoma

115

399

257

124

168

Kilimanjaro

68

51

105

57

43

Lindi

537

284

99

243

235

Mara

123

119

243

129

205

Mbeya

189

341

241

277

427

Morogoro

226

229

155

205

164

Mtwara

183

209

160

106

180

Mwanza

315

161

232

191

215

Coast

127

120

232

164

141

Rukwa

216

166

250

206

190

Ruvuma

636

341

331

201

198

Singida

68

159

104

329

353

Shinyanga

249

377

251

262

193

Tabora

160

154

126

149

132

Tanga

140

196

299

249

235

Average, Mainland

167

197

190

190

215

Source: Mandara and Kaisi (1991) and MOH, Family Planning Programme (1992)
Sepsis after surgery which contributes about 30 percent of maternal deaths and the major direct obstetric cause, is nutrition related in the sense that most surgical interventions are done due to obstructed labour caused by a narrow pelvis. Haemorrhage and rupture of the uterus, the second and third respective direct causes of maternal mortality are also related to maternal malnutrition caused by frequent pregnancies which weaken the uterus. According to the DHS data, the average birth interval for Tanzania Mainland and Zanzibar were 33.3 and 30.4 months respectively. Surprisingly, the DHS data show that on average women have the number of children they wish to have. The ideal wish was 6 and the total fertility rate was 6.2. Moreover, a large number of women in the rural areas have more than 8 pregnancies in their reproductive period. High fertility is associated with high rates of maternal mortality for two reasons: it increases the number of times a woman faces the risks of pregnancy and child birth and it increases the likelihood that the pregnancy will be “high risk”. In addition, despite the availability of family planning services the rate of use of modern services of contraception is extremely low rising from as low as 5-7 percent during the late 1980’s to only about 10 percent during the early 1990s. The problem is compounded by the severe problems faced by the health delivery system due to lack of essential supplies and equipment, inadequate transport facilities including for referral, problems of staff shortage and lack of motivation.

Figure 4: The main causes of maternal deaths in hospitals in mainland Tanzania, 1986

Source: M. Muru, “Maternal Mortality: How Much is Known about It?”.
Because of the generally low food intake and their heavy workload many women are left physically exhausted by the frequent pregnancies which do not give them enough time to build up their nutritional reserves and thus enter each next pregnancy in an unhealthier condition. This results in the phenomenon called the “maternal depletion syndrome” which makes them even more susceptible to disease. The very high rates of anaemia of pregnancy worsen the risks of childbirth. Even small loses of blood are dangerous for women with anaemia.

Perinatal mortality

An important outcome of pregnancy is the survival of the newborn. The concept of Safe Motherhood encompasses the safety of both the mother and the new born baby. The safety of the newborn can be gauged from its birth weight, which we have already discussed and its survival during the first week of life. The perinatal mortality rate is the number of deaths (including still births) within the first week of life per 1,000 live births.

Information on perinatal mortality in Tanzania is lacking. The few available information (Table 26) indicate that the rate is high and is associated with the birth weight of the baby which as we have seen is an indicator of maternal nutrition.

Table 26: Perinatal mortality rate (PNMR) per 1,000 live births according to birth state and birth weight, Ilula Village, Iringa

Birth State

PNMR per 1,000

PNMR according to birth weight in grams

1000-1999 (n=27)

2000-2499 (n=48)

2500-4500 (n=596)

Unknown (n=61)

Singletons (n=682)

67

667

111

24

333

Twins (n=682)

280

583

238

0

500

Total births (n=732)

82

630

170

24

344

Source: Moller et al (1988)

Infant and Under-five year child mortality

Studies done in Tanzania in children aged 6-30 months show that the relative risk of death is more than twice for moderate malnutrition and about eight times for severe malnutrition as compared to the well nourished group [Yambi, 1988]. The mortality rate of children under-five years of age (U5MR) can thus be used as a proxy of the gravity of the problem of malnutrition.

The national trends of both Infant Mortality Rate (IMR) and Under-five year Mortality Rate (U5MR) in Tanzania over the last four decades (table 27) shows that the mortality rates have been declining continuously between the 1950’s and 1980’s. The proportion of children dying between birth and their first year of life per 1,000 live births (IMR) declined from 19 percent in the early 1960s to 11.5 percent in the mid-eighties. Likewise the proportion of children who die between birth and their fifth birthday per 1,000 live births (U5MR) fell from over 30 percent in the 1950’s to about 19 percent in the late 1980’s. The 1985 IMR and U5MR are within the upper end of the range for Eastern African countries and in the middle range for the whole of Africa. But they are still very high by World standards and Tanzania remains among the countries in the world continuously classified as very high U5MR countries by UNICEF.

Table 27: The trends of life expectancy, IMR and U5MR in Tanzania (1957-1988)

Year

Life expectancy at birth (years)

Infant Mortality Rate (IMR), (0/1,000)

Child Mortality rate (CMR), (1-4 yrs) 0/1,000

Under-five Mortality Rate (U5MR) 0/1,000

1957 Census

35

190

110

300

1967 Census

41

160

101

261

1978 Census

51

137

100

231

1988 Census

54

115

76

191

Source: URT, Bureau of Statistics (Population Census 1967-1988)
If the past trends of IMR and U5MR are projected to the year 2,000, an IMR of 88 will be achieved against a goal of 50; while an U5MR of 145 will be achieved against a goal of 70 (fig. 5). In order to achieve the national goals annual reduction rates of 5.7 for IMR and 6.9 for U5MR will be needed against a past trend of 1.8 percent and 1.9 percent respectively. The obvious implication for Tanzania is that more deliberate efforts need to be undertaken if the adopted goals are to be achieved.

Figure 5: National IMR and U5MR Trend for Tanzania (1961-2000)

National IMR Trend

National U5MR Trend

Source: United Republic of Tanzania, Bureau of Statistics, 1978 and 1988 Population Census

* Represents only Tanzania Mainland

The trends of mortality and life expectancy shown in table 27 and figure 5 indicate that since independence in 1961 to 1985 there was a substantial improvement in the quality of life of the average Tanzanian. The substantial decrease in the Infant Mortality rate (IMR) of 39.5 percent and the rather slow decrease in the Child Mortality Rate (CMR) of 33.7 percent reflect that the improvements which took place were mainly the impact of improved health care rather than an improvement in the child’s nutritional health and overall safety. This is also borne out by the nutritional status data which as we have already discussed showed a stable trend over time and place until 1985 onwards when specific large scale nutrition programmes were undertaken.

As for maternal mortality an analysis of the IMR and U5MR for the 1988 census data estimates show wide regional variations (table 28).

Table 28: Regional Infant and Under-five Mortality Rates in Tanzania

Region

Infant Mortality Rate (IMR, 0/1,000)

Under Five Mortality Rate (U5MR), 0/1,000

Average annual reduction in U5MR

1975

1985

1975

1985

1975-1985

to reach 70/1000 by the year 2000

Arusha

108

75

179

119

4.2

3.6

Coast

121

113

204

189

0.8

6.9

Dar es Salaam

108

105

179

173

0.3

6.2

Dodoma

133

132

225

222

0.1

8.0

Iringa

152

130

257

220

1.6

7.9

Kagera

133

130

225

219

0.3

7.9

Kigoma

163

115

269

192

3.4

7.0

Kilimanjaro

76

67

119

104

1.4

2.3

Lindi

151

140

255

236

0.8

8.4

Mara

140

125

236

211

1.1

7.6

Mbeya

161

124

267

209

2.5

7.6

Morogoro

140

125

236

211

1.1

7.6

Mtwara

161

138

267

233

1.4

8.4

Mwanza

139

115

233

192

2.0

7.0

Rukwa

170

131

283

221

2.5

8.0

Ruvuma

145

113

245

188

2.7

6.8

Shinyanga

150

110

252

183

3.3

6.6

Singida

137

96

231

157

3.9

5.5

Tabora

140

101

236

166

3.6

5.9

Tanga

112

106

187

176

0.6

6.3

Tanzania Mainland

137

115

231

191

1.9

6.9

Source: United Republic of Tanzania, Bureau of Statistics, 1978 and 1988 population census
While the 1978 census data showed that the southern and western regions of the country had much higher rates of mortality than the northern and central regions; the 1988 data showed the southern, central and lake zone regions had higher rates than the other regions. The regions with the lowest mortality rates were Kilimanjaro, Arusha and Singida with U5MR of less than 160; those with relatively low levels of between 160 and 180 were Tabora, Dar es Salaam and Tanga. Twelve regions had an U5MR of between 180 and 215. The regions with the highest U5MR of above 215 were Lindi, Mtwara, Dodoma, Rukwa, Iringa and Kagera.

However, there has been a decline of IMR and U5MR in all regions. The rates of decline vary. Regions with relatively rapid rates of decline (2.5 percent or more per annum) were Arusha, Singida, Tabora, Kigoma, Shinyanga, Mbeya and Rukwa. Regions with very little change (less than one percent per annum) include Dar-Es-Salaam, Kagera, Dodoma, Tanga, Lindi and Coast. Because of so little change some regions like Dodoma and Kagera which had been slightly better off than the national average in 1978, were much worse off in 1988. Conversely, regions like Singida, Tabora and Shinyanga which were worse off than the average in 1978, were better off than the national average in 1988.

A further desegregation of the 1988, IMR and U5MR data on a district level show even wider disparities than those observed at the regional level. While the regional variation for IMR and U5MR range from 67 and 104 (Kilimanjaro) to 140 and 236 (Lindi) respectively; the district variation ranges from 46 and 68 (Ngorongoro) to 167 and 281 (Mbeya Rural) respectively. Wide district variations occur even within the same region. There are also significant rural/urban variations for both mortality and malnutrition rates(table 29).

Table 29: Rates of infant and child mortality in rural and urban Tanzania

Area

Infant Mortality Rate
(IMR) 0/1,000

Underfive mortality rate
(U5MR) 0/1,000

Malnutrition
(W/A below -2SD)

Rural

138

249

29.2

Urban

104

179

27.2


The likely explanation for these disparities are related to differences in income, education and the frequency of feeding young children rather than patterns of food availability, population growth or disease.

If the mortality reduction rates for 1975-85 are maintained, only Arusha region can achieve the IMR and U5MR goals of 50 and 70 respectively, by the year 2,000. Regions like Kilimanjaro and Singida could achieve the goals with a little more concerted effort. However, for the remaining 17 regions, dramatic efforts in according child survival priority will be needed. That infant and young child mortality rates declined during the economically crisis period of 1975-85 is another indication that the crisis did not significantly affect rural life.


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