3
Mortality Trends

CHILD MORTALITY

Child Mortality Before the Mid-1970s

Prior to the National Demographic Survey (NDS) of 1977 and the Kenya Fertility Survey (KFS) of 1977–1978, the only information on child mortality that covered all or most of the country came from the censuses. The first census in 1948 included questions on lifetime births to mothers, deaths at under 1 year of age, and deaths at over 1 year. These questions were asked in a purposive sample of communities. There was a broad age division of mothers between those within the reproductive ages and those past them. (Ages in calendar years were recorded for women who knew them, but the numbers were very small and unevenly distributed over the country.) From the responses, crude estimates of child mortality can be derived. At the censuses of 1962, 1969, and 1979, data were collected for all women in the reproductive period, by age, of total children born, surviving, and dead. The proportion dead by age group of mothers can be translated into approximate estimates of child mortality at different times by standard indirect methods.1 The measures provide mortality trends over some 30 years from the 1940s.

1  

The procedure used here is the Brass child survival method (1964), which converts proportions of children ever born who have died, reported by women in five-year age groups, into estimates of the probability of dying (by age 5 in this report). In essence, the procedure uses



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 25
Population Dynamics of Kenya 3 Mortality Trends CHILD MORTALITY Child Mortality Before the Mid-1970s Prior to the National Demographic Survey (NDS) of 1977 and the Kenya Fertility Survey (KFS) of 1977–1978, the only information on child mortality that covered all or most of the country came from the censuses. The first census in 1948 included questions on lifetime births to mothers, deaths at under 1 year of age, and deaths at over 1 year. These questions were asked in a purposive sample of communities. There was a broad age division of mothers between those within the reproductive ages and those past them. (Ages in calendar years were recorded for women who knew them, but the numbers were very small and unevenly distributed over the country.) From the responses, crude estimates of child mortality can be derived. At the censuses of 1962, 1969, and 1979, data were collected for all women in the reproductive period, by age, of total children born, surviving, and dead. The proportion dead by age group of mothers can be translated into approximate estimates of child mortality at different times by standard indirect methods.1 The measures provide mortality trends over some 30 years from the 1940s. 1   The procedure used here is the Brass child survival method (1964), which converts proportions of children ever born who have died, reported by women in five-year age groups, into estimates of the probability of dying (by age 5 in this report). In essence, the procedure uses

OCR for page 25
Population Dynamics of Kenya Figure 3-1 Trends in child mortality—indirect estimates of proportions dying. These data have been thoroughly analyzed, particularly by John Blacker and colleagues (1987). Results are available down to the district level, although they are not fully published. Aggregate estimates of mortality up to age 5 years from the censuses (including a measure from the rather different 1948 source) are given in Figure 3-1. They show a consistency among the different censuses that is remarkable relative to experience with the methods in other developing countries, particularly in Africa. Thus the estimate for 1954 from the 1969 census (reports of women aged 45–49 years) is almost identical to the level at that time indicated by the 1962 census (women aged 30–34 and 35–39 years). Similarly, the estimate for 1964 from 45-to 49-year-old women at the 1979 census is in excellent   an adjustment factor to translate the proportion dead into a life table value of mortality. The adjustment factors are determined by the shape of the fertility schedule (United Nations, 1983). The resulting mortality estimates can be affected by sampling variation and reporting errors, such as the underreporting of children ever born, age misreporting, and the inclusion of stillbirths and adoptions in the number of children ever born. In addition, there are a couple of important assumptions underlying this procedure: (1) that rates of change in infant and child mortality and fertility are constant in the recent period before the survey and conform to model schedules, and (2) that there is no association between a child's mortality risk and the mother's age or mortality.

OCR for page 25
Population Dynamics of Kenya TABLE 3-1 Indirect Estimates of Probability of Dying by Age 5 (5q0) per 1,000 Births from Census Reports of Children Who Have Died Approximate Year of Estimate 5q0 Decrease per Year in Interval Year of Census Data Source 1940 270 — 1948 1954 239 2.2 1969 1964 197 4.2 1979 1974 153 4.4 1979 agreement, with the points in the early 1960s derived from reports at the 1962 and 1969 censuses. Even the measure obtained from the 1948 census sample located around 1940 (not shown), which is less secure, is in accord with the extrapolation backward from later points. It can be concluded with confidence that the trend demonstrated by the series is valid, although reservations should be noted. There could be biases in the measures from errors common to censuses, for example, in the classification of stillbirths or the choice of model patterns of mortality by age in the estimation procedure. There are several alternative methods for arriving at estimates of the probability of dying by age 5 (5q0) and their time locations. However, the range of values from different assumptions is much smaller than the changes in incidence shown by the trend. Child mortality fell from about 250 deaths per 1,000 births in 1950 to almost 150 by 1975, or at a rate of about 4 deaths per 1,000 births per year. Because the proportion of children dead for an age group of mothers is an average over a substantial range of child birth cohorts, the measures at calendar years are smoothed values around these points. The dips and upturns in the estimates from the reports for the younger mothers, apparent at all the censuses, can be attributed to selection by birth order and possibly social and economic factors such as illegitimacy. A fitted trend should smooth out these deviations. The trend is very nearly linear from 1950, and because this trend is based on a series of overlapping measures from three censuses there is no reason to believe it is an artifact of the method of estimation. Table 3-1 gives the estimates of 5q0 at selected times based on the census data. Child Mortality After the Mid-1970s Between 1977 and 1984 there was a series of national surveys that collected information on children born and died by age of mother. Two of these, the 1977–1978 KFS and the 1984 Kenya Contraceptive Prevalence Survey (KCPS) were conducted with comparatively small samples (about

OCR for page 25
Population Dynamics of Kenya TABLE 3-2A Indirect Estimates of Probability of Dying by Age 5 (5q0) from Reports of Proportions of Children Who Have Died: Comparisons of Three Surveys and 1979 Census Data   1978 KFS 1983 NDS 1984 KCPS 1979 Census Age of Mother Year of Estimate 5q0 Year of Estimate 5q0 Year of Estimate 5q0 Year of Estimate 5q0 20–24 1975 .156 1980 .124 1981 .143 1976 .152 25–29 1973 .158 1978 .121 1979 .167 1974 .153 30–34 1970 .154 1976 .132 1977 .143 1972 .166 35–39 1968 .164 1973 .137 1975 .171 1970 .169 40–44 1965 .164 1971 .147 1972 .176 1967 .185 45–49 1962 .188 1967 .157 1968 .176 1964 .198

OCR for page 25
Population Dynamics of Kenya 8,100 and 6,600 women interviewed, respectively) but obtained complete maternity histories. In the first survey, ages at death of children were also recorded and mortality rates in the time periods can be calculated directly. Three rounds of the NDS were completed in 1977, 1978, and 1983. There were problems with the analysis of the data from the first two dates, but some results are available for 1977. These are very similar to the measures estimated from the KFS but are rather erratic. They are not presented here. Table 3-2A gives estimates of 5q0 derived from the proportions of children dead by age group of mothers, reported in three surveys, and compares them with the measures from the 1979 census. The values are also plotted in Figure 3-2. The agreement among the four sets of indirect estimates is not as good as would be hoped. In assessing their characteristics it should be noted that the three surveys had essentially the same sampling frame, which excluded seven of the more remote districts of the country, as noted in Chapter 2. However, because only about 5 percent of the Kenyan population lived in these districts, the bias introduced in comparisons with the census results is very small. The sample errors of the estimates from the KFS and KCPS are considerable and may explain the erratic features of the individual measures of 5q0. The census estimates are in best agreement overall with the KCPS Figure 3-2 Trends in child mortality—indirect estimates of proportions dying.

OCR for page 25
Population Dynamics of Kenya child mortality levels, with both indicating quite a steep decrease in rates in the 10 years from the late 1960s. The measures derived from the reports of younger women in the KFS, providing child mortality levels around the mid-1970s, are also close to the census estimates. However, the KFS measures from reports of older women are considerably lower than the census values at equivalent times. The trend from the KFS is much flatter and fits rather poorly with the pattern of declines deduced from the 1962, 1969, and 1979 censuses. The outlying set of estimates is from the 1983 NDS, the general level being 20 to 30 deaths per 1,000 lower than the census rates, although the trend downward with time is steeper than for the KFS values. Although there is some uncertainty about childhood mortality in the 1970s, a 5q0 of about 155 deaths per 1,000 seems acceptable for around 1975. This estimate is in accord with the data from the 1979 census and the KFS. The proportions of children reported as dead by older women at the KFS may be a little understated, but the discrepancy here could be due to sample error. The level of child mortality shown by the NDS estimates is so inconsistent with the evidence from other sources that it must be rejected. There is no plausible explanation of why the NDS measures should be right, and the remaining series all show higher proportions of child mortality to much the same extent. Of the surveys reviewed in Figure 3-2, only the KFS recorded dates of births and deaths of children. It is thus possible from these data to calculate the child mortality in calendar periods directly. (The estimates presented thus far have relied on indirect techniques and data that do not allow direct calendar period estimates.) Figure 3-3 compares the direct and indirect estimates from the KFS. The agreement is satisfactory. In accordance with the foregoing discussion, the KFS reports of child deaths in a recent period before the survey (here taken as 10 years) can serve as a reliable base for the examination of subsequent trends. As discussed in Chapter 2, the Kenya Demographic and Health Survey (KDHS) of 1988–1989 collected maternity histories with details of births and deaths of children from 7,150 women aged 15–49 years. The sample frame was the same as for the KFS; the questions and procedures for obtaining information on child deaths were also very similar, but in the later survey there was a substantial additional section on child health. The comparability of data and coverage in the two surveys makes them the obvious sources for the measurement of child mortality changes over the 11-year interval. Table 3-2B gives the indirect estimates of 5q0 from data from the KFS and KDHS on proportions of children dead by age of mothers. The results are disquieting. The mortality rate in the mid-1970s of around 100 deaths per 1,000 births from the KDHS is only two-thirds of the rate from the KFS for the same period. The relative underreporting of child deaths in the

OCR for page 25
Population Dynamics of Kenya Figure 3-3 Estimates of 5q0 (probability of dying by age 5) from 1978 KFS. KDHS can be seen even more directly and obviously. At the KFS, 1.20 children per woman aged 35–39 years were reported to have died. Eleven years later at the KDHS, these women would have been 46–50 years old with additional deaths of children, including some of those born in the interval but failing to survive the heavy risks of infancy. However, only 1.08 dead children were recorded per woman aged 45–49 years. It does not necessarily follow from the heavy relative underreporting of child deaths at the KDHS by the older women that there was a similar error for the younger women. The time trend in the 5q0 derived from the age groups of mothers in the KDHS is effectively zero. The implication that there was no decline in child mortality over the 15 years or so before the date of the KDHS is, of course, completely at variance with the comparative levels of the two surveys and other evidence of regular improvements. Indeed, if a rate of decline in 5q0 of 4 deaths per 1,000 per year is extrapolated from the experience of the 20 years before 1975 (based on census data), the resulting level of mortality by the mid-1980s is not very far from the KDHS estimates from the recent reports. More incisive comparisons of the recording of child deaths at the KFS and KDHS are given in Tables 3-3A and 3-3B. Here the data from the later

OCR for page 25
Population Dynamics of Kenya TABLE 3-2B Indirect Estimates of Probability of Dying by Age 5 (5q0) from Reports of Proportions of Children Who Have Died, 1978–1989: KFS and KDHS Data   1978 KFS 1989 KDHS Age of Mother Year of Estimate Children Dead per Woman 5q0 Year of Estimate Children Dead per Woman 5q0 20–24 1975 0.23 .156 1986 0.14 .113 25–29 1973 0.54 .158 1984 0.28 .092 30–34 1970 0.85 .154 1982 0.52 .106 35–39 1968 1.20 .164 1979 0.67 .097 40–44 1965 1.45 .164 1977 0.84 .097 45–49 1962 1.88 .188 1973 1.08 .110

OCR for page 25
Population Dynamics of Kenya TABLE 3-3A Child Deaths per Woman Reported from Births in Approximately the Same Periods—KDHS Estimates as a Percentage of KFS Estimates   Years Before KDHS Age of Women at KDHS 10–14 15–19 20–24 25–29 30–34 More than 10 Under 35 91 99a       93 35–39 69 72 81a     72 40–44 72 73 69 65a   70 45–49 96 60 64 68 60a 67 a Including small numbers from earlier periods. survey are backdated 10 years, and measures are calculated for 5-year age cohorts and preceding periods. This process is essentially a reconstruction of the KDHS results relevant to the years prior to 1979, slightly more than a year later than the KFS. Table 3-3A shows the ratios of child deaths reported per woman at the KDHS to those at the KFS for age cohorts of women in preceding time periods. Although the ratios are erratic in some of the marginal cells where the numbers of child deaths are low, the general pattern is coherent. The relative underreporting in the KDHS is about 30 percent for women aged 35 years and over, with only a slight tendency to become worse as age increases. At ages under 35 years, the discrepancy is much less, but because in this comparison only children of mothers under 25 years at the KFS are covered, the numbers of deaths are fairly small. A significant feature is the lack of any clear tendency for the ratios to be smaller for reports from the more distant past when the age group of the TABLE 3-3B Proportions of Children Reported Dead at KDHS and KFS from Births for Approximately the Same Time Periods and Cohorts of Mothers by Age Group Age of Mothers at KDHS KFS at Survey Date 10 Years Before KDHS KDHS as a Percentage of KFS 25–29 .102 .087 85 30–34 .130 .107 82 35–39 .143 .103 72 40–44 .155 .115 74 45–49 .175 .127 73

OCR for page 25
Population Dynamics of Kenya mothers is fixed (comparing across the table). In Table 3-3B, the proportions of children dead as recorded at the two surveys for approximately the same cohorts and time periods are displayed. The findings are in close accord with the conclusions from Table 3-3A. The measures for the older cohorts are about 27 percent lower from the KDHS than the KFS, with a reduced shortfall for the younger cohorts. The discrepancies between the reporting of child deaths in the two surveys decrease as the age of mother decreases and also as births become more recent since the two characteristics are strongly correlated. It should be remembered, however, that the comparisons are of deaths from births that occurred 10 more years before the KDHS than the KFS. Differentials in Child Mortality Table 3-4 gives the comparisons of proportions of children reported dead at the KFS and KDHS for approximately the same time periods and cohorts by province, residence, and education. The cohorts distinguished are the mothers under and over 35 years at the later survey. The numbers of child deaths for the former category are frequently rather small for the earlier date. Although the ratios of the measures at the two surveys are fairly erratic, as expected in view of the sample errors, there is little indication of substantial variations in the relative completeness of reporting. Notably, the educational groups are in good agreement, with the smallest overall discrepancy in fact for the women with no education. The ratios for the relative proportions of child deaths reported are higher for urban than for rural women but only modestly; this suggests that reporting in urban areas may be slightly better than in rural areas. For the provinces there are two aberrant indices. In the Coast, the proportions of children reported as dead by mothers under 35 years at the KDHS were much higher than at the KFS, but the measures for mothers over 35 were in accord with the general pattern. The recorded proportions dead in Rift Valley for mothers over 35 at the KDHS were exceptionally low. Although notable, these deviations do not provide an adequate base for the derivation of incompleteness assessments that differ for subgroups. The reasons for the severe underreporting of child deaths at the KDHS have not been established. One possibility is that the addition of more detailed questions on birth intervals, contraception, and illness of children overloaded the interviewers. There is no direct evidence that overloading of the questionnaire affected the completeness of the reporting of deaths of children who were born in the 10 years before the KDHS. The pattern of error does not rule out the possibility that the reports for the recent period were correct. There is, in fact, some evidence supporting this conclusion from the distribution of child deaths over the 10 years before the surveys.

OCR for page 25
Population Dynamics of Kenya TABLE 3-4 Proportions of Children Reported Dead at KDHS and KFS from Births for Approximately the Same Time Periods and Cohorts of Mothers by Province, Residence, and Education   Mothers Under Age 35 Mothers Age 35 and Over Residence and Education KFS KDHS KDHS as Percentage of KFS KFS KDHS KDHS as Percentage of KFS Province             Kenya .125 .102 82 .158 .115 73 Nairobi .083 .075 90 .129 .078 60 Central .080 .045 56 .099 .080 81 Coast .139 .265 191 .201 .168 84 Nyanza .192 .142 74 .225 .179 80 Eastern .091 .065 72 .142 .120 85 RiftValley .084 .063 75 .106 .046 43 Western .143 .107 75 .194 .141 73 Residence             Urban .100 .085 85 .121 .098 81 Rural .128 .104 81 .162 .116 72 Education             None .161 .135 84 .184 .140 76 1–4 years .138 .126 91 146 .097 66 5+ years .092 .060 66 .109 .079 72

OCR for page 25
Population Dynamics of Kenya environment, and economic development, others are very diverse. Measures for districts have been calculated by Blacker and Airey3 from the census reports by mothers of children born and died. Their results are not fully published but have been utilized in papers. Very similar results have been obtained by Ewbank et al. (1986). Unfortunately, it is not possible at present to extend these estimates to the late 1970s and 1980s. Calculations from the 1983 NDS have been made, but as shown previously the mortality levels are too low to be consistent with the other series. In any case the estimates would be brought forward only a few years. The combination of the underreporting of child deaths and the small sample sizes for districts in the 1989 KDHS makes the derived 5q0 too unreliable for interpretation. However, the strong correlation between the child mortality trends in the provinces from 1973 to 1984 and from 1954 to 1973 suggests that the district trends for the earlier years are broadly valid for the more recent period also. Of course there could still be individual exceptions. Evidence in appendix Table 3A-1 supports the general conclusion by demonstrating that the decreases in child mortality by district in Rift Valley Province in 1954 to 1964 were highly correlated with the declines in 1964 to 1974. It seems reasonable therefore to take the trends for 1954 to 1974 as good indicators of the variations in improvement by district over the past 30 years or so. Many of the districts show series of 5q0 by period from the censuses that are in excellent agreement, as was the case for the national measures. In some cases the consistency is not so impressive. Examples in both categories are given in the appendix. In all, however, the calculations based on data from the 1969 and 1979 censuses provide a convincing trend. Table 3-7 presents estimates of 5q0 in 1954 and 1974 by district and the ratios of the latter values to the former. In all cases the 1974 measure is derived from the proportion of children reported as dead by mothers aged 25–29 years at the 1979 census. The corresponding child mortalities for provinces are not the same as the values for 1973 obtained from the KFS and utilized in Table 3-5, but the differences are remarkably small with the exception of Rift Valley Province where the 1979 census estimate is considerably higher than the KFS value (132 per 1,000 compared with 95 per 1,000). In this province, unlike the others, the proportion of dead children from births 5 to 10 years before the survey is exceptionally low, with an upward jump at 10 to 15 years. The value of 95 per 1,000 is likely to be an underestimate, but the gap to 132 is uncomfortably large. The estimates of 5q0 in 1954 are derived from the reports of proportions of children dead by women aged 45–49 and 40–44 years at the 1969 census. In most districts the measure taken was that calculated from child deaths for the oldest group 3   Thousands of calculations for population subgroups by different methods were made available to the working group from the files of Blacker and Airey.

OCR for page 25
Population Dynamics of Kenya TABLE 3-7 Estimates of Trends in Probability of Dying by Age 5 (5q0) by District Province District 1954 1974 Ratio 1974/1954 Nairobi Nairobi .133 .112 84 Central Kiambu .168 .077 46   Kirinyaga .255 .117 46   Muranga .214 .089 42   Nyandarua .188 .083 44   Nyeri .167 .062 37 Coast Kilifi .254 .236 93   Kwale .229 .224 98   Lamu .198 .198 100   Mombasa .168 .140 83   Taita .275 .142 52   Tana River .177 .189 107 Eastern Embu .217 .110 51   Isiolo .229 .153 67   Kitui .263 .177 67   Machakos .209 .119 57   Marsabit .152 .144 95   Meru .178 .103 58 Northeastern Garissa .176 .148 84   Mandera .141 .157 111   Wajir .207 .153 74 Nyanza Kisii .234 .133 57   Kisumu .337 .243 72   Siaya .355 .252 71   South Nyanza .370 .262 71 Rift Valley Baringo .206 .189 92   Elgeyo Marakwet .111 .150 135   Kajiado .146 .088 60   Kericho .156 .115 74   Laikipia .168 .098 58   Nakuru .190 .120 63   Nandi .187 .130 70   Narok .158 .120 76   Samburu .089 .098 110   Trans Nzoia .215 .138 64   Turkana .173 .158 91   Uasin Gishu .159 .114 72   West Pokot .253 .230 91 Western Bungoma .258 .170 66   Busia .362 .236 65   Kakamega .271 .168 62

OCR for page 25
Population Dynamics of Kenya of mothers (45 to 49 years). In a minority of districts, the series of 5q0 values at time points derived from the 1979 and 1969 (and, in some instances, 1962) censuses indicated a trend line fitting poorly the estimate from the oldest group of mothers. In eight of these ten districts (as indicated in the table), the 5q0 measure obtained from the reports of the 40-to 44-year-old mothers was selected as a better estimate for 1954, that is, more consistent with other points. In the other two districts, Marsabit and Laikipia, an average of the values from mothers aged 45–49 years and 40–44 years was taken. Although Marsabit is in Eastern Province, it is geographically in the remote north and was excluded from the KFS and KDHS samples. Three of the other districts with inconsistent reports of proportions of children dead by the oldest mothers in 1969 are in the Coast Province where improvement in child mortality was slight. The modified procedure of estimation makes the indices slightly more favorable but does not change the conclusion of poor gains. The other six districts are all in the diverse Rift Valley Province. Two of them, Baringo and Turkana, can be assessed in much the same way as the Coast Province districts above, but Narok, Laikipia, Trans Nzoia, and Uasin Gishu showed moderate improvements in child mortality and would still have done so, although to a different degree, if the estimation procedure had not been modified. In the discussion of the levels and trends of 5q0 by district, emphasis is placed on the cohesion or discrepancies with the provincial findings in Table 3-5. The outstanding gains in Central Province were shared by all of its districts fairly equally but with some advantage to Nyeri, both in level (62 per 1,000 in 1974) and in trend (a 63 percent improvement from 1954 to 1974). The three districts of Western Province were also notably similar in the 5q0 trends, with reductions of about one-third from the high levels of 1954. The three districts in Eastern Province that adjoin Central (Embu, Machakos, and Meru) experienced falls in child mortality that were almost as great as for districts in Central and achieved respectably low levels in 1974. The areas of Isiolo and Kitui had average performances. As noted above, Marsabit is a geographically remote district compared with the rest of Eastern Province. Its child mortality appears to have improved little, although the 1974 level is not exceptionally high, when compared, for example, to most of Nyanza and Western provinces. The comments on Marsabit also apply to the three districts of Northeastern Province, which are also remote and were excluded from the KFS and KDHS samples. For these districts, there must be some suspicion about the accuracy of the estimates based on the reports of older women at the 1969 census and hence of the derived trends. In 1954, Nyanza Province exhibited clearly the highest 5q0 but by 1984 it had moved to a less extreme ranking. This change in ranking was due to average progress in three of the four districts (some 30 percent reduction

OCR for page 25
Population Dynamics of Kenya from 1954 to 1974) and an impressive mortality decline of 43 percent in Kisii to a level of 133 deaths per 1,000, only about one-half of the measure in the rest of the province. Only one district in the Coast Province contradicted the trend as the province moved from average child mortality in 1954 to the highest incidence in 1974. This district was Taita-Taveta, the most distant from the Indian Ocean, where child mortality almost halved from 1954 to 1974 although from an initially high level. The 13 districts of the Rift Valley Province stretch from Kajiado, bordering Tanzania in the central-south to Turkana touching the Sudan in the remote northwest. Not surprisingly, the child mortality levels and trends are very diverse. The three districts that adjoin the Central Province (Nakuru, Laikipia, and Kajiado) experienced strong improvement to low levels, whereas the five in the northwest (Baringo, Elgeyo Marakwet, West Pokot, Samburu, and Turkana) made little gain. The remainder, lying between the central area and the west did comparatively well, although the mortality reductions were not as large as in Central Province and its immediately surrounding areas. The Possible Effects of AIDS on Child Mortality There has been increased concern about the spread of acquired immune deficiency syndrome (AIDS) in Kenya, as well as other regions of sub-Saharan Africa. One study of blood donors conducted in Nairobi indicated that the prevalence rate of human immunodeficiency virus (HIV), which causes AIDS, was 6.2 for men and 2.9 for women. In Nyanza and Coast provinces, the prevalence of HIV infection in blood donors was 4.3 and 3.5 percent, respectively. HIV seroprevalence rates for prostitutes in Nairobi have risen dramatically between 1980 and 1990, from 7.1 to 87.8 (Center for International Research, 1991). There is little evidence on the effects of AIDS on child mortality in Kenya. There are certainly both direct and indirect effects. HIV is transmitted directly from infected mothers to their children during delivery (about 30 percent of the time), and possibly through breastfeeding. Children who are HIV negative, but whose mothers suffer from or die of AIDS face additional mortality risks from being orphaned (Working Group on the Effects of Child Survival and General Health Programs on Mortality, 1993). The mortality rates examined in this chapter are mainly for periods some time in the past when the effects of AIDS were very small. Recent child mortality rates most likely have been affected by AIDS, but the estimated addition to deaths is still probably small relative to the degree of measurement error. For Africa as a whole, the Working Group on the Effects of Child Survival and General Health Programs on Mortality (1993) estimates that AIDS is the primary cause of death in about 3 percent of all infant and child deaths.

OCR for page 25
Population Dynamics of Kenya ADULT MORTALITY Information on adult mortality in sub-Saharan Africa is limited and uncertain. It is difficult to draw firm conclusions from the evidence for a single country such as Kenya. In particular, although broad trends in adult mortality for subnational aggregates can be explored, any findings are greatly restricted in time and must be hedged with cautions. A general review of the topic over a range of countries is given by Timæus (1993). The 1969 and 1979 censuses of Kenya included questions on the survivorship of parents. Both Ewbank et al. (1986) and Blacker et al. (1987), by similar techniques, used the responses to estimate the levels of adult mortality for Kenya as a whole as well as for its provinces and districts. A primary objective of the latter authors was to examine the relation between adult and childhood mortality. The account here is based on their estimates. The method used was developed by Timæus (1986), following suggestions made by Preston and Bennett (1983). The proportion of persons in each age group with mothers (fathers) alive at the two censuses were averaged and adjusted to produce a single set of measures for females (males) TABLE 3-8 Estimates of Adult Mortality, 1969–1979, Both Sexes Province and District Expectation of Life at Age 15, e(15) Province and District Expectation of Life at Age 15, e(15) Kenya 52.9 Western 54.1 Nairobi 54.5 Bungoma 54.8 Central 55.7 Busia 50.7 Kirinyaga 57.1 Kakamega 54.9 Muranga 55.5 Northeastern 50.8 Kiambu 54.0 Garissa 49.9 Nyandarua 56.7 Mandera 50.6 Nyeri 56.3 Wajir 51.9 Eastern 54.4 Rift Valley 54.4 Embu 54.9 Baringo 50.5 Isiolo 50.5 Elgeyo Marakwet 52.4 Kitui 53.9 Kajiado 52.5 Machakos 55.5 Kericho 53.6 Marsabit 51.1 Laikipia 55.0 Meru 53.6 Nakuru 54.7 Coast 53.9 Nandi 54.7 Kilifi 54.0 Narok 56.6 Kwale 53.6 Samburua 51.9 Lamu 52.2 Trans Nzoia 54.0 Mombasa 52.7 Turkanaa 45.1 Taita Tavetaa 50.7 Uasin Gishu 54.9 Tana Rivera 46.5 West Pokot 49.8 a Estimates based on 1979 orphanhood data only.

OCR for page 25
Population Dynamics of Kenya for the intercensal period. These proportions were then translated into probabilities of survival by the original weighting method devised by Brass and Hill (1973). The probabilities were finally converted into equivalent values of life expectancies at age 15, e(15), by using the logit model life table system. The resulting e(15) values were 55.2 and 50.5 years for females and males, respectively, for the country as a whole. The corresponding measures for 1979 derived by Ewbank et al. (1986) using slightly different methods were 55.2 and 51.4, in good agreement. Table 3-8 presents the e(15) values for provinces and districts. The measures for the two sexes have been combined. Although the variations in differentials by sex over the districts are by no means extraordinary, it is considered that little weight can be put on their precise values. The provincial indices of e(15) are all close to the country-wide level. Although Central Province with the lowest child mortality has the highest life expectancy at age 15 (55.7 years), this level is only slightly greater than the measures for Western and Coast provinces, whose probabilities of child death by age 5 are more than double that of Central. The only province with an appreciably lower e(15) is the remote Northeastern Province where, in any case, the estimates are of doubtful reliability. In Figure 3-4, the Figure 3-4 Child and adult mortality by district, specified districts, and rest of Kenya.

OCR for page 25
Population Dynamics of Kenya e(15) measures are plotted against the 5q0 estimates for 1974 by district. There is a general relationship of the kind expected but of very moderate strength. This result is perhaps not surprising in view of the comparatively small range of the e(15)s and their likely errors. The main divergences from a broadly linear relationship between adult and child mortality are for the remote districts of the north (indicated by squares). All seven of these, the areas not included in the KFS and KDHS samples, have adult life expectancies that are low relative to their 5q0 estimates. It is possible that these inconsistencies are due to underreporting of child deaths, but it is equally plausible that nomadic ways of life lead to differences in the life table patterns. Some, but not all, of the districts in Coast and Western provinces present the reverse phenomenon, comparatively high adult life expectancies with heavy child mortality as in Kilifi and Kwale (shown as triangles), and Kakamega and Bungoma (pluses). SUMMARY From 1950 to 1975, child mortality in Kenya fell from about 250 to about 155 deaths per 1,000 births or at a rate of approximately 4 deaths per 1,000 each year. Analysis of the Kenya Demographic and Health Survey indicates substantial underreporting of child deaths, particularly by women aged 35 years and over, which makes the exact measurement of child mortality more difficult for the recent period. However, the evidence points to a more rapid decline from the mid-1970s to the mid-1980s to about 110 deaths per 1,000 (adjusted for underreporting) in 1984. Mortality declines by province for the two periods, 1954–1973 and 1973–1984, were highly correlated, suggesting that the factors affecting decreased child mortality were similar for both periods. The pattern of declines between these two periods resulted in a greater variation among the provinces in child mortality in the 1980s than in the 1950s. The sizes of the reductions in mortality were similar for both urban and rural areas, although differentials in mortality by residence remain. Differentials in mortality by education were reduced, and remarkable gains were made in the mortality of children for mothers with no education.

OCR for page 25
Population Dynamics of Kenya APPENDIX TABLE 3A-1 Estimates of Trends in the Probability of Dying by Age Five (5q0) by District   Ratio Province and District A 1954 B 1964 C 1974 B/A C/B C/A Central Kirinyaga .255 .184 .117 72 64 46 Muranga .214 .148 .089 69 60 42 Kiambu .168 .116 .077 69 66 46 Nyandarua .188 .135 .083 72 61 44 Nyeri .167 .111 .062 66 56 37 Coast Kilifi .254 .250 .236 98 94 93 Kwale .229 .232 .224 101 97 98 Lamua .198 .198 .198 100 100 100 Mombasaa .168 .154 .140 92 91 83 Taita .275 .206 .142 75 69 52 Tana Rivera .177 .219 .189 124 86 107 Eastern Embu .217 .162 .110 75 68 51 Isiolo .229 .189 .153 83 81 67 Kitui .263 .219 .177 83 81 67 Machakos .209 .157 .119 75 76 57 Marsabitb .152 .150 .144 99 96 95 Meru .178 .148 .103 83 70 58 Northeastern Garissa .176 .160 .148 91 92 84 Mandera .141 .167 .157 118 94 111 Wajir .207 .164 .153 79 93 74 Rift Valley Baringoa .206 .209 .189 101 90 92 Elgeyo .111 .156 .150 141 96 135 Kajiado .146 .103 .088 71 85 60 Kericho .156 .132 .115 85 87 74 Laikipiab .168 .141 .098 84 70 58 Nakuru .190 .158 .120 83 76 63 Nandi .187 .163 .130 87 80 70 Naroka .158 .139 .120 88 86 76 Samburu .089 .109 .098 122 90 110 Trans Nzoiaa .215 .176 .138 82 79 64 Turkanaa .173 .173 .158 100 91 91 Uasin Gishua .159 .147 .114 92 78 72 West Pokot .253 .263 .230 104 97 91 a 1954 measure is estimated from the proportion of children dead for women aged 40–44 years. b 1954 measure is average of estimates for women aged 40–44 and 45–49 years.

OCR for page 25
Population Dynamics of Kenya Figure 3A-1 Child mortality estimates—Kajiado district. Figure 3A-2 Child mortality estimates—Kiambu district.

OCR for page 25
Population Dynamics of Kenya Figure 3A-3 Child mortality estimates—Kwale district. Figure 3A-4 Child mortality estimates—Tana River district.

OCR for page 25
Population Dynamics of Kenya Figure 3A-5 Child mortality estimates—Trans Nzoia district.