Child mortality in rural Malawi: HIV closes the survival gap between the socio-economic strata.

Abstract

As HIV-related deaths increase in a population the usual association between low socioeconomic status and child mortality may change, particularly as death rates from other causes decline.
As part of a demographic surveillance system in northern Malawi in 2002-6, covering a population of 32,000, information was collected on socio-economic status of the households. Deaths were classified as HIV/AIDS-related or not by verbal autopsy. Poisson regression models were used to assess the association of socio-economic indicators with all-cause mortality, AIDS-mortality and non-AIDS mortality among children. There were 195 deaths in infants, 109 in children aged 1-4 years, and 38 in children aged 5-15. All-cause child mortality in infants and 1-4 year olds was similar in households with higher and lower socio-economic status. In infants 13% of deaths were attributed to AIDS, and there were no clear trends with socio-economic status for AIDS or non-AIDS causes. For 1-4 year olds 27% of deaths were attributed to AIDS. AIDS mortality was higher among those with better built houses, and lowest in those with income from farming and fishing, whereas non-AIDS mortality was higher in those with worse built houses, lowest in those with income from employment, and decreased with increasing household assets.
In this population, since HIV infection among adults was initially more common among the less poor, childhood mortality patterns have changed. The usual gap in survival between the poor and the less poor has been lost, but because the less poor have been disproportionately affected by HIV, rather than because of relative improvement in the survival of the poorest.

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Child Mortality in Rural Malawi: HIV Closes the Survival
Gap between the Socio-Economic Strata
Andreas Jahn1, Sian Floyd1, Nuala McGrath1,2, Amelia C. Crampin1, Lackson Kachiwanda3,
Venance Mwinuka3, Basia Zaba1, Paul E. M. Fine1, Judith R. Glynn1*
1Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom, 2Africa Centre for Health and
Population Studies, University of KwaZulu Natal, Mtubatuba, South Africa, 3Karonga Prevention Study, Chilumba, Karonga, Malawi
Abstract
Background: As HIV-related deaths increase in a population the usual association between low socioeconomic status and
child mortality may change, particularly as death rates from other causes decline.
Methods/Principal Findings: As part of a demographic surveillance system in northern Malawi in 2002-6, covering a
population of 32,000, information was collected on socio-economic status of the households. Deaths were classified as HIV/
AIDS-related or not by verbal autopsy. Poisson regression models were used to assess the association of socio-economic
indicators with all-cause mortality, AIDS-mortality and non-AIDS mortality among children. There were 195 deaths in infants,
109 in children aged 1–4 years, and 38 in children aged 5–15. All-cause child mortality in infants and 1–4 year olds was
similar in households with higher and lower socio-economic status. In infants 13% of deaths were attributed to AIDS, and
there were no clear trends with socio-economic status for AIDS or non-AIDS causes. For 1–4 year olds 27% of deaths were
attributed to AIDS. AIDS mortality was higher among those with better built houses, and lowest in those with income from
farming and fishing, whereas non-AIDS mortality was higher in those with worse built houses, lowest in those with income
from employment, and decreased with increasing household assets.
Conclusions/Significance: In this population, since HIV infection among adults was initially more common among the less
poor, childhood mortality patterns have changed. The usual gap in survival between the poor and the less poor has been
lost, but because the less poor have been disproportionately affected by HIV, rather than because of relative improvement
in the survival of the poorest.
Citation: Jahn A, Floyd S, McGrath N, Crampin AC, Kachiwanda L, et al. (2010) Child Mortality in Rural Malawi: HIV Closes the Survival Gap between the Socio-
Economic Strata. PLoS ONE 5(6): e11320. doi:10.1371/journal.pone.0011320
Editor: James Holland Jones, Stanford University, United States of America
Received March 15, 2010; Accepted May 31, 2010; Published June 28, 2010
Copyright: ? 2010 Jahn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The Karonga Prevention Study is funded primarily by the Wellcome Trust (www.wellcome.ac.uk), with contributions from LEPRA (The British Leprosy
Relief Association, www.lepra.org.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Judith.glynn@lshtm.ac.uk
Introduction
Associations between socioeconomic status and mortality have
long been recognised. Among rich and poor populations even fine
gradations in relative poverty have been associated with
differences in survival.[1,2] It is less clear if this has always been
the case.[3] Pre-1900, being able to reach or afford health care
would have been of limited benefit, and in predominantly rural
communities, exposure to crowds and infectious diseases may have
been limited among the poor.
The HIV epidemic has produced some parallels with this
historical situation. In many populations HIV initially affected the
better off, more educated and more mobile sectors of the
community.[4,5,6] And before the widespread availability of
effective antiretrovirals and prevention of mother-to-child trans-
mission, access to health care had limited impact on HIV-related
mortality.
In populations with high HIV prevalence, HIV is the
predominant cause of death in young adults, and an important
cause in young children–directly, through vertical trans-
mission, and indirectly, through orphanhood and parental
illness.[7] This may be sufficient to attenuate, or even reverse,
the expected associations between socio-economic status and
mortality.
In a rural area of northern Malawi we have documented
mortality patterns through a demographic surveillance system over
the period leading up to the wide-spread use of antiretrovirals.
HIV infection was first recorded in this area in 1982.[8] The
prevalence increased slowly in the 1980s, and reached a plateau of
10–11% in adults from the mid 1990s.[8,9]
In the adult population in this area, in the early stages of the
HIV epidemic in 1988–90, HIV infection was more common
among non-farmers, those with higher education and among
people born outside the district.[9] A decade later, in a stable
generalised epidemic, occupation other than farming remained
associated with higher prevalence, and HIV infection was more
common among adults with higher and with no education,
compared with individuals with only primary education.[9]
We have already shown that just before the introduction of
antiretrovirals, two thirds of deaths in those aged 15–59 were
attributable to AIDS.[10] We now assess the association between
socio-economic factors and child mortality in this population.
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Methods
Ethics statement: The work was approved by the Health
Sciences Research Committee, Malawi and by the ethics
committee of the London School of Hygiene and Tropical
Medicine, UK. The methods of the demographic surveillance
system have been described in detail elsewhere.[11] Before the
start of the study the Traditional Authority that covers the area,
and all village headmen and traditional advisors in the study area
were informed about the aims of the study and the nature of the
data to be collected, and their approval and verbal consent was
sought. All household members were given a similar explanation
and interviews were only conducted if verbal consent was given by
the household head and by the respective household members.
The consent was recorded by the interview sheet being filled.
Refusals were recorded in field registers. During the baseline
census 15 households did not provide verbal consent and have
consequently been excluded from the study. The data for this
study come from the basic demographic surveillance for which the
ethics committees agreed that written consent was not needed.
After an initial house-to-house census, a continuous registration
system was set up in a population of around 32,000. Trained
community members (‘‘village informants’’) recorded births,
deaths and migrations against pre-printed lists for their area.
Each informant covered 15–60 households. Births and deaths
were followed up monthly, and migrations annually, by project
staff. A re-census after two years showed that the routine system
had registered 97% of births, 99% of deaths, and 92% of
migrations.[11]
Verbal autopsy interviews were conducted in the local language
by a medical assistant or health worker with the mother or most
immediate caregiver. Verbal informed consent was obtained
before the interview. The verbal autopsies were similar to the
INDEPTH tool,[12] an adaptation of the standard WHO
questionnaire, with different forms for neonatal and other child
deaths. Information from patient-held health documents and
hospital records was used when available. Three individuals
(doctors or clinical officers) coded the results independently.
Information on maternal HIV status from previous or other
concurrent studies in the population was used by the reviewers
where available. HIV/AIDS was assigned as a cause of death if
clinical symptoms suggested immunosuppression in the absence of
other obvious causes (e.g. underlying malnutrition), taking account
of any available information on maternal HIV infection or AIDS
death, and any prior diagnoses of suspected HIV infection.
Discrepantly coded cases were discussed, and resolved where
possible. Socio-economic and other household data were not
available to the reviewers of the verbal autopsies.
Socio-economic data were collected at the household level
during the baseline census and at the registration visit for in-
migrating, newly-formed, or moved households. Several measures
of socio-economic status were used: education level of the mother
and father; main source of income of the household; household
possessions; and dwelling construction. Parental education status
was only known if the parent was included in the baseline census
or registration visit, and so was missing for parents who had died
before this time, and for those who were not normally resident in
the demographic surveillance area.
Previous field work and a pilot census had shown that there was
little formal employment and that for their livelihood households
commonly relied on a variety of different income-generating
activities in addition to subsistence agriculture. Specific income-
generating activities were initially collected as free-text descriptions
during the pilot census and then classified into 11 categories: sale
of own agricultural produce, sale of own fish, regular employment,
small scale trading, piecework, manufacturing, preparation and
sale of snacks or drinks (commonly doughnuts, beer or local spirit),
letting of houses or land, providing a service (waiter, hairdresser,
etc.), gathering natural products (firewood, charcoal, grass for
thatching, etc.). Households were asked to name all income
generating activities they had engaged in over the last 12 months
and then to rank the listed activities by importance. For the
analysis, the main source of household income was grouped into 6
categories (farming, fishing, employment, piecework, small trading
and other).
A standard inventory of household possessions appropriate to
the setting (bicycle, oxcart, motorbike, watch or clock, radio,
mosquito net, canoe, fishing net, cattle) was converted into an
approximate monetary value. To avoid over-estimating the wealth
of households with many cattle, the asset category was calculated
first without the cattle, and then increased by a maximum of one
or two categories according to the number of cattle owned.
Dwelling construction was classified according to the materials
used to make the roof, walls and floor. The ‘‘best’’ houses had
concrete floors, iron sheet or tile roofs, and burnt brick or concrete
walls. The ‘‘worst’’ had mud floors, no iron sheet or tiles on the
roof, and walls of unburnt brick, mud, or other materials.
Statistical analysis
All analyses were carried out in Stata 10.0 (Stata Corporation,
USA). Observation time for each child was defined from the time
they were first seen in the baseline census, from birth (if the mother
was a member of a household in the observation area at the time
of birth) or from the time of migration into the area after the
baseline census. Observation time ended at the time of death (if the
child was still a member in a household in the area at the time of
death) or at the time of migration out of the surveillance area.
Multiple episodes of observation and gaps were allowed if children
moved out of the surveillance area and later returned.
For the estimation of AIDS mortality, the failure event was
death from AIDS, while deaths from all other causes (known or
unknown) were censored. For the estimation of mortality from
other causes (‘non-AIDS mortality’), the failure event was death
from causes other than AIDS, and deaths from AIDS and from
unknown causes were censored.
The age groups ,1, 1–4 and 5–14 years were analysed
separately as mortality rates are dramatically different in these age
groups. The reliability of verbal autopsy and the relative
contribution of HIV to mortality are also likely to vary.
Poisson regression models were used to assess the association of
mortality with background characteristics. Age, sex and area of
residence were considered as a priori confounders. The overall
strength of association for each variable was assessed using the
likelihood ratio test, comparing the model with and without the
variable of interest. Formal tests for trend across categories were
conducted where appropriate. Analyses were repeated using Cox
models, to allow for changing rates by age, and using robust
standard errors to allow for non-independence of data from
children with the same mother. The results were almost identical;
only the Poisson regression results are presented.
Results
Between August 2002 and the end of February 2006, a total of
20,389 children under 15 years were observed in the study
population, of whom 10,415 were under 5 years and 5,237 were
under 1 year. There were 195 deaths in infants (,1 year), 109 in
those aged 1–4 years, and 38 in those aged 5–14 years.
Child Mortality and HIV
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Tables 1 and 2 show the association of socio-economic factors
with all cause mortality rates by age group. There were small
numbers of children with missing information on socio-economic
status, but these children tended to have higher mortality rates.
There was little evidence of the usual trend of higher mortality in
lower socioeconomic groups in any age group. Because of the low
numbers of deaths in the older children, associations with socio-
economic status were only explored further in those under 5 years.
In Tables 3, 4, 5 and 6 and figure 1, deaths are shown overall
and separately for those attributed by reviewers to AIDS and to
non-AIDS. Associations with socio-economic factors are shown
separately for infants (tables 3 and 4) adjusted for sex and area of
residence, and for children aged 1–4 years (tables 5 and 6),
adjusted for year of age, sex and area of residence.
Among the infants, 22 deaths were attributed to AIDS, 167 to
non-AIDS and 6 to unknown causes. The mother was known to be
HIV positive by the time of birth in 7/22 AIDS deaths and 13/
167 non-AIDS deaths. Mortality rates were higher in those whose
fathers had 5–8 years primary education than in those with more
or less (p=0.02), and this pattern persisted in those thought to
Table 1. Age-specific mortality (per 1000 person years observation [PYO]) from all causes by parental education.
0–11 months1–4 years5–14 years
D/PYORate 95% CID/PYORate 95% CID/PYORate 95% CI
Mother’s education
None/primary 1–4 20/34857.4 37.1–89.0 13/128810.1 5.86–17.4 2/29030.69 0.17–2.76
primary 5–8131/239354.8 46.1–65.0 64/79648.04 6.29–10.2720/13,3021.50 0.97–2.33
secondary +
38/76549.6 36.1–68.2 22/213110.3 6.80–15.73/19371.55 0.50–4.80
unknown6/57 11047.5–235 10/620 16.1 8.68–30.0 13/4910 2.65 1.54–4.56
Father’s education
None/primary 1–4 8/20339.4 19.7–78.911/65918.8 9.24–30.10/1400 0.0
primary 5–8104/170061.2 50.5–74.1 43/52668.16 6.06–11.0 11/90681.21 0.67–2.19
secondary +
51/129539.4 29.9–51.837/37329.91 7.18–13.7 9/48621.85 0.96–3.56
unknown32/36687.5 61.9–124 18/2345 7.68 4.84–12.2 18/77242.33 1.47–3.70
doi:10.1371/journal.pone.0011320.t001
Table 2. Age-specific mortality (per 1000 person years observation [PYO]) from all causes by household characteristics.
0–11 months 1–4 years5–14 years
D/PYO Rate 95% CID/PYORate 95% CI D/PYORate 95% CI
Household assets
$ 0-43/68662.7 46.5–84.5 27/215512.58.59–18.36/40071.50 0.67–3.33
$5-44/772 57.042.4–76.6 21/2463 8.535.56–13.1 10/42142.371.28–4.41
$10- 57/122646.535.9–60.341/42149.737.16–13.2 13/7677 1.690.98–2.92
$50-24/54943.7 29.3–65.3 12/19666.10 3.47–10.74/41520.960.36–2.57
$ 150-22/268 82.1 54.1–1245/1020 4.902.04–11.8 5/25831.94 0.81–4.65
unknown 5/6280.733.5–1943/18416.35.26–50.60/417
Dwelling class
1 (Best)27/56847.532.6–69.3 19/2079 9.145.83–14.310/48522.061.11–3.83
2 25/40761.9 41.9–91.712/14128.504.83–15.09/3091 2.911.51–5.60
3 58/111951.840.1–67.0 33/3862 8.54 6.07–12.08/74161.080.54–2.16
4 (Worst) 79/141655.844.8–69.6 44/44509.89 7.36–13.311/72701.51 0.84–2.73
unknown6/56 106.8 48.0–2381/2005.01 0.71–35.6 0/4220.0-
Household main source of income
farming78/148252.642.2–65.7 48/50129.58 7.22–12.710/9479 1.060.57–1.96
employment19/48339.325.1–61.711/1785 6.163.41–11.1 6/3882 1.550.69–3.44
piecework17/35747.6
29.6–76.5 9/10898.274.30–15.95/15833.16 1.31–7.59
small trading 24/45253.1 35.6–79.212/14128.504.83–15.0 5/2474 2.020.84–4.85
fishing23/329 69.946.5–105.3 7/1072 6.533.11–13.74/1850 2.16 0.81–5.76
other 32/41676.954.4–10921/149414.19.16–21.67/34752.010.96–4.23
unknown2/44 46.011.5–184 1/138 7.261.02–51.51/309 3.240.46–23.0
doi:10.1371/journal.pone.0011320.t002
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have died from non-AIDS causes (p=0.06). There were no strong
or statistically significant associations with any of the other
measures of socio-economic status.
Among children aged 1–4 years, 27% of deaths were attributed
to AIDS (28/104 with known causes; 5 unknown). Of the 28 AIDS
deaths, 9 had a mother who was known to be HIV positive at the
time of death, though only 4 were known to have been positive at
the time of birth. Five of 76 children dying of non-AIDS causes
had HIV positive mothers.
There was a ‘‘U-shaped’’ pattern of all-cause mortality
associated with parental education, with the lowest levels in those
with mothers or fathers with 5–8 years primary education
(figure 1). When disaggregated by cause of death, the higher rates
in those with more educated parents were only seen for AIDS
mortality, but the associations were weak. A trend to lower
mortality rates in those with more valuable household possessions
was stronger for non-AIDS mortality (p trend=0.006) than for all-
cause mortality (p trend=0.03); there was no evidence of a similar
trend for AIDS mortality. While overall mortality was similar in
those with different dwelling constructions (p=1.0), mortality rates
from AIDS were higher among those from the better built houses
(p=0.0004), and mortality rates from other causes were higher
among those living in houses with inferior construction (p=0.03).
Overall mortality was similar in households with different main
sources of income, but AIDS mortality was lowest in those with
income from farming and fishing, and highest in those with
income from employment and ‘‘other’’ sources, and non-AIDS
mortality was lowest in those with income from employment.
Table 4. Association of all cause, AIDS and non-AIDS mortality with household characteristics in infants.
All cause mortalityAIDS mortality Non-AIDS mortality
D/PYORate Rate Ratio[95% CI]D/PYORate Rate Ratio[95% CI]D/PYORate Rate Ratio[95% CI]
Household assets
$ 0-43/68662.71P=0.25/686 7.31P=1.0 35/68651.01P=0.2
$5-44/772 57.00.91 0.60–1.39 5/7726.50.86 0.25–2.9839/77250.51.00 0.63–1.58
$10- 57/122646.5 0.750.51–1.126/1226 4.90.70 0.21–2.3150/1226 40.80.810.53–1.25
$50- 24/549 43.70.700.43–1.163/549 5.50.730.17–3.09 21/549 38.30.760.44–1.31
$ 150- 22/26882.1 1.310.78–2.201/2683.7 0.59 0.069–5.0820/268 74.61.450.83–2.52
Dwelling class
1 (Best)27/568 47.5 0.840.53–1.31 3/568 5.30.680.18–2.58 21/56836.90.780.47–1.29
2 25/40461.91.100.69–1.74 4/404 9.9 1.380.42–4.5921/40452.0 1.10 0.67–1.81
3 58/111951.8 0.890.63–1.26 5/11194.50.610.20–1.85 53/111947.40.970.67–1.40
4 (Worst)79/1416 55.8 1P=0.79/1416 6.41P=0.667/141647.31 P=0.7
Household main source of income
farming78/148252.61P=0.1 4/1482 2.71P=0.2 78/1482 48.61 P=0.1
employment 19/48339.3 0.830.50–1.40 4/4838.3 3.35 0.78–14.419/483 31.10.700.40–1.24
piecework17/35747.6 0.98 0.58–1.672/3575.6 2.250.40–12.617/357 39.2 0.860.48–1.54
small trading 24/45253.11.13
0.70–1.82 5/45211.14.57 1.15–18.224/452 37.60.850.49–1.47
fishing23/329 69.91.51 0.92–2.491/3293.01.220.13–11.7 23/329 66.91.56 0.93–2.61
other32/416 76.91.65 1.07–2.54 5/416 12.0 4.831.22–19.2 32/41662.5 1.410.88–2.26
Rate ratios are adjusted for sex and area. Deaths with unknown causes are censored in the analysis of AIDS and non-AIDS mortality.
doi:10.1371/journal.pone.0011320.t004
Table 3. Association of all cause, AIDS and non-AIDS mortality with parental education in infants.
All cause mortality AIDS mortalityNon-AIDS mortality
D/PYORateRate Ratio[95% CI]D/PYORateRate Ratio [95% CI] D/PYORate Rate Ratio [95% CI]
Mother’s education
None/primary 1–420/348 57.4 1.140.66–1.982/3485.7 0.910.18–4.6018/348 51.71.240.69–2.24
primary 5–8131/239354.81.100.77–1.59 13/23935.40.79 0.30–2.10 113/2393 47.21.15 0.77–1.72
secondary +
38/76549.61 P=0.86/7657.8 1P=0.931/765 40.51P=0.7 [0.4 tr]
Father’s education
None/primary 1–4 8/20339.40.95 0.45–2.011/203 4.91.99 0.22–18.0 7/20334.50.910.41–2.02
primary 5–8104/1700 61.2 1.551.10–2.1710/17005.9 2.110.66–6.79 90/170052.91.481.03–2.11
secondary +
51/129539.41 P=0.024/12953.1 1 P=0.4 46/129535.51 P=0.06
Rate ratios are adjusted for sex and area. Deaths with unknown causes are censored in the analysis of AIDS and non-AIDS mortality.
doi:10.1371/journal.pone.0011320.t003
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‘‘Other income sources’’ included activities such as providing
services (hairdressing, waitressing, etc.), producing and selling
snacks or beer and relying on support from outside relatives.
Among the 1–4 year olds who died, 18% of AIDS deaths and 75%
of non-AIDS deaths were in those with the main family income
from farming.
Discussion
We have shown a marked shift from the usually observed
pattern of socio-economic determinants of child mortality, in a
population with high HIV prevalence and declining background
levels of child mortality. In Karonga District in the 1990s we
observed lower under-5 mortality in those whose fathers were
traders or salaried workers compared to subsistence farmers or
casual labourers, and lower mortality in those whose mothers had
higher levels of education [13]. In the current study, factors such
as higher maternal education, improved housing, and source of
income, were not strong predictors of lower child mortality when
mortality from all causes was considered.
Under-5 mortality in this population was 85 per 1000 births, lower
than the national average estimate from the 2004 DHS (133 per
1000),[14] but comparable with levels recorded in 2004 in
neighbouring Tanzania (83 per 1000 births).[15] Adult HIV
prevalence has been around 12% since 2000 in Malawi [7] and in
2005 only 7% of HIV-infected mothers were estimated to have
received ARV prophylaxis for the prevention of mother-to-child
transmission.[7] Although the HIV epidemic is likely to have a severe
Table 5. Association of all cause, AIDS and non-AIDS mortality with parental education in children 1–4 years of age.
All cause mortalityAIDS mortality Non-AIDS mortality
D/PYORateRate Ratio[95% CI]D/PYORate Rate Ratio[95% CI]D/PYORateRate Ratio[95% CI]
Mother’s education
None/primary 1–413/1288 10.10.99 0.49–1.993/12882.330.880.23–3.41 9/12887.001.130.47–2.71
primary 5–864/79648.00.800.49–1.31 12/79641.510.500.20–1.2450/79646.28 1.09 0.58–2.06
secondary +
22/2131 10.31P=0.068/21313.75 1 P=0.312/21315.631 p=1.0
Father’s education
None/primary 1–4 11/65916.71.69 0.86–3.33 4/6596.1 3.02 0.94–9.767/659 10.6 1.520.65–3.55
primary 5–843/52668.20.820.53–1.28 5/52660.95 0.430.15–1.28 36/52666.84 1.000.59–1.69
secondary +
37/37329.91P=0.110/37322.71P=0.0224/37326.43 1 P=0.6
Rate ratios are adjusted for sex, age in one-year intervals, and area. Deaths with unknown causes are censored in the analysis of AIDS and non-AIDS mortality.
doi:10.1371/journal.pone.0011320.t005
Table 6. Association of all cause, AIDS and non-AIDS mortality with household characteristics in children 1–4 years of age.
All cause mortalityAIDS mortalityNon-AIDS mortality
D/PYORate Rate Ratio[95% CI] D/PYORate Rate Ratio [95% CI]D/PYO RateRate Ratio [95% CI]
Household assets
$ 0- 27/2155 12.51P=0.2 [0.03tr] 7/21553.21 p=0.8 20/2155 9.281P=0.02 [0.006tr]
$5- 21/24638.5 0.660.37–1.285/2463 2.00.590.19–1.86 15/2463 6.09 0.650.33–1.26
$10-41/4214 9.70.79 0.48–1.2810/4214 2.4 0.750.28–1.97 29/4214 6.880.760.43–1.34
$50-12/1966 6.1 0.520.26–1.033/19661.5 0.46 0.12–1.798/19664.07 0.480.21–1.10
$ 150-5/10204.90.400.15–1.053/1020 2.9 1.020.26–3.95 1/10200.980.11 0.015–0.81
Dwelling class
1 (Best) 19/20799.1 1.030.59–1.80 12/2079 5.8 3.631.35–9.775/2079 2.40.35 0.13–0.90
212/14128.5 0.970.51–1.86 7/1412 5.03.161.07–9.34 5/1412 3.50.530.20–1.36
333/38628.5 0.950.60–1.50 2/38620.520.34 0.70–1.6630/3862 7.8 1.100.20–1.36
4 (Worst)44/44509.91P=1.07/44501.61 P=0.000435/4450 7.91p=0.03 [0.01 tr]
Household main source of income
farming48/50129.61P=0.35/50121.01P=0.0242/5012 8.41 p=0.005
employment 11/17856.20.690.35–1.367/1785 3.9 3.551.07–11.71/17850.560.074 0.010–0.54
piecework9/10898.30.870.42–1.783/1089 2.8 2.660.62–11.46/10895.5 0.670.28–1.59
small trading12/1412 8.5 0.930.48–1.79 3/14122.11.97
0.45–8.599/14126.40.810.38–1.73
fishing 7/1072 6.50.840.36–1.921/1072 0.90.78 0.087–7.056/1072 5.60.980.39–2.43
other21/1494 14.11.570.91–2.70 9/1494 6.05.98 1.90–18.911/1494 7.4 0.94 0.46–1.90
Rate ratios are adjusted for sex, age in one-year intervals, and area. Deaths with unknown causes are censored in the analysis of AIDS and non-AIDS mortality.
doi:10.1371/journal.pone.0011320.t006
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Figure 1. Mortality rates by socio-economic status and cause in infants and children age 1–4 years, Karonga District Malawi, 2002-6.
doi:10.1371/journal.pone.0011320.g001
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impact on child mortality in Malawi, overall levels of mortality have
steadilydeclined.[14] This can only be explained by a large decline in
non-HIV related mortality, and with declining levels of background
mortality, the relative impact of HIV is likely to increase.[16]
ThecomparativeanalysisofriskfactorsforAIDS-andnon-AIDS
mortality revealed that the absent or weak associations of socio-
economicfactorswith childmortalityfrom allcauseswereexplained
by the differential distribution of AIDS and non-AIDS deaths in the
population. Almost all AIDS deaths in children in this population
are likely to have been caused by transmission of HIV from mother
to child and the distribution of paediatric AIDS deaths is therefore
likely to mirror the distribution of HIV infection in women of
reproductive age. It is possible that women of higher socio-
economic status are more likely to be HIV tested, and knowledge of
the mother’s HIV status may have influenced classification of the
death, but only about one third of the AIDS deaths in those under 5
years were in those whose mothers were known to be HIV-infected.
In contrast to the 2004 Malawi DHS, in which children of
mothers with secondary school education had approximately 40%
lower infant mortality and 60% lower under 5 mortality,[14]
maternal education in the study population was only weakly
associated with all cause mortality in infants or children. Infants
and children of mothers with secondary education had the highest
rates of AIDS mortality and the lowest rates of non-AIDS
mortality, but both trends were weak. Trends with paternal
education were also weak, and differed in infants and children. In
both age groups there were high death rates in those with
unknown maternal education, reflecting high mortality among
maternal orphans (not shown).
Measurement of economic status at the household level in this
study included ownership of household assets and dwelling
construction, adapted to the local setting. Such indicators have
shown close association with school enrolment and moderate
correspondence with the traditional wealth indicators of household
expenditure in India, Indonesia, Pakistan and Nepal,[17] and with
HIV prevalence in sub-Saharan Africa.[18] In the current study,
among 1–4 year olds, while there was no association of overall
mortality with dwelling characteristics, there was a trend towards
lower mortality with increasing asset value (p=0.03). Separation into
AIDS and non-AIDS mortality showed a clearer pattern, with high
AIDSmortalityinthosewithhigherscoringdwellings,andhighnon-
AIDS mortality in those with lower scoring dwellings and few assets.
For 1–4 year olds the pattern of deaths by household income
source reflects the distribution of HIV in the population, with AIDS
deaths dominating in households with employment, and non-AIDS
deaths among those relying on subsistence farming. Higher HIV
prevalence in wealthier adults (using quintiles derived from an asset-
based wealth index) has been shown in DHS+ surveys from 8
countries in sub Saharan Africa, including in Malawi. [18]
There are no clear patterns for association with the household
level measures for infants either overall or when divided into AIDS
and non-AIDS mortality. This may reflect the greater difficulty of
accurate assignment of cause of death as AIDS or non-AIDS in
this age group. The sensitivity and specificity of verbal autopsy for
the classification of infant and child AIDS deaths are poor,[19]
[20] [21] although better when, as here, the autopsy interview is
carried out soon after death. [22] The resulting misclassification is
likely to have weakened the estimated effects of factors that had
differential associations with AIDS and non-AIDS mortality. It is
also possible that better-off HIV positive children are more likely
to survive beyond infancy, reducing the association between socio-
economic status and AIDS deaths in the infant age group, and
magnifying it in the 1–4 year olds from the deferred deaths.
There was evidence that registration of births and deaths was
near complete, from the re-census, from scheduled follow-ups of
births after 1 year and from the distribution of ages at death. This
makes it unlikely that child deaths were selectively missed (e.g.
from poorer households and in remote areas). Parental and
household characteristics were obtained by linking to other
individuals in the study population. 95% of the mothers and
85% of the fathers of children below 5 years of age whose parents
were not known to have died at baseline were among the
population under surveillance, resulting in a high degree of
completeness of information on parents. Asset score and main
source of income were determined by interview, while dwelling
construction was determined by direct observation by the field
staff.
It is possible that other factors led to the lack of association
between socio-economic status and child mortality. Differential
migration could affect the trend if, for example, better off
individuals stay in the district because of sickly children, but
migration could affect all socio-economic levels.
The finding of the expected trends of higher non-AIDS
mortality in children of households with few assets, poor dwelling
construction, and relying on subsistence farming, suggests that the
socio-economic indicators are measuring what they are supposed
to measure. The lack of association with overall mortality is
explained by the different trends in AIDS deaths. AIDS mortality
was much higher in children from higher socio-economic status
households, mirroring the distribution of HIV infection in the
adult population. The usual and unwelcome gap in survival
between the poor and the less poor has been lost, but because the
less poor have been disproportionately affected by HIV, rather
than because of relative improvement in the survival of the
poorest.
Since HIV infection is associated with relative wealth in many
parts of sub-Saharan Africa, [18] a change in the distribution of
child mortality is likely in other settings. The extent to which it is
apparent will depend on the relative contribution of AIDS and
non-AIDS deaths, so it is most likely in settings and age groups in
which background mortality rates are low.
It is probably a temporary phenomenon. A shift in the
distribution of HIV in the population towards the poorer
individuals has been predicted, [18] and a shift from the most
educated groups earlier in the epidemic, to the less educated later,
has been seen in several sub-Saharan African populations.[6] The
survival gap is likely to return, and uneven access to antiretrovirals
is likely to exacerbate the divide.
Acknowledgments
We thank the Government of the Republic of Malawi for their interest in
and support of the Project and the National Health Sciences Research
Committee of Malawi for permission to publish the paper.
Author Contributions
Conceived and designed the experiments: AJ ACC BZ PEMF JRG.
Performed the experiments: AJ NM ACC LK VM. Analyzed the data: AJ
SF BZ JRG. Contributed reagents/materials/analysis tools: LK VM.
Wrote the paper: AJ SF NM ACC PEMF JRG.
References
1. van Rossum CT, Shipley MJ, van de Mheen H, Grobbee DE, Marmot MG
(2000) Employment grade differences in cause specific mortality. A 25 year
follow up of civil servants from the first Whitehall study. J Epidemiol
Community Health 54: 178–184.
Child Mortality and HIV
PLoS ONE | www.plosone.org7 June 2010 | Volume 5 | Issue 6 | e11320

Page 8

2. Gwatkin D, Rutstein S, Johnson K, Suliman E, Wagstaff A, et al. (2007) Socio-
economic differences in health, nutrition, and population within developing
countries. An overview: World Bank.
3. Razzell P, Spence C (2005) Social capital and the history of mortality in Britain.
Int J Epidemiol 34: 477–478; author reply 479-480.
4. Over M, Piot P (1993) HIV infection and sexually transmitted diseases. In:
Jamison DT, Mosley WH, Mensham AR, Bobadilla JL, eds. Disease control
priorities in developing countries. Oxford: Oxford University Press. pp 455–527.
5. Wojcicki JM (2005) Socioeconomic status as a risk factor for HIV infection in
women in East, Central and Southern Africa: a systematic review. J Biosoc Sci
37: 1–36.
6. Hargreaves JR, Bonell CP, Boler T, Boccia D, Birdthistle I, et al. (2008)
Systematic review exploring time trends in the association between educational
attainment and risk of HIV infection in sub-Saharan Africa. AIDS 22: 403–414.
7. UNAIDS (2008) Report on the global AIDS epidemic 2008. Geneva: Joint
United Nations Programme on HIV/AIDS.
8. Glynn JR, Ponnighaus JM, Crampin AC, Sibande F, Sichali L, et al. (2001) The
development of the HIV epidemic in Karonga District, Malawi. AIDS 15:
2025–2029.
9. Crampin AC, Glynn JR, Ngwira B, Mwaungulu F, Ponnighaus JM, et al. (2003)
Trends and measurement of HIV prevalence in northern Malawi, 1988–2001.
AIDS 17: 1817–1825.
10. Jahn A, Floyd S, Crampin AC, Mwaungulu F, Mvula H, et al. (2008)
Population-level effect of HIV on adult mortality and early evidence of reversal
after introduction of antiretroviral therapy in Malawi. Lancet 371: 1603–1611.
11. Jahn A, Crampin AC, Glynn JR, Mwinuka VM, Mwaiyeghele E, et al. (2007)
Evaluation of a Village-Informant Driven Demographic Surveillance System.
Demographic Research 16: 219–248.
12. INDEPTH Network (2003) INDEPTH Standardized Verbal Autopsy question-
naire (Revised August 2003). .
13. Crampin AC, Floyd S, Glynn JR, Madise N, Nyondo A, et al. (2003) The long-
term impact of HIV and orphanhood on the mortality and physical well-being of
children in rural Malawi. AIDS 17: 389–397.
14. Mwale MW (2005) Chapter 8: Infant and child mortality. In:National Statisitcs
Office [Malawi] OM, editor. Malawi Demographic and Health Survey 2004.
Calverton, Maryland, USA and Zomba, Malawi. pp 123–132.
15. Masanja H, de Savigny D, Smithson P, Schellenberg J, John T, et al. (2008)
Child survival gains in Tanzania: analysis of data from demographic and health
surveys. Lancet 371: 1276–1283.
16. Marston M, Zaba B, Salomon JA, Brahmbhatt H, Bagenda D (2005) Estimating
the net effect of HIV on child mortality in African populations affected by
generalized HIV epidemics. J Acquir Immune Defic Syndr 38: 219–227.
17. Filmer D, Pritchett LH (2001) Estimating wealth effects without expenditure
data—or tears: an application to educational enrollments in states of India.
Demography 38: 115–132.
18. Mishra V, Bignami-Van Assche S, Greener R, Vaessen M, Hong R, et al. (2007)
A study of the association of HIV infection with wealth in sub-Saharan Africa.
DHS Working Papers 31.
19. Nykanen M, Tamaona W, Cullinan T, Van Oosterzee V, Ashorn P (1995)
Verbal autopsy as a technique to establish causes of infant and child mortality.
East Afr Med J 72: 731–734.
20. Todd JE, De Francisco A, O’Dempsey TJ, Greenwood BM (1994) The
limitations of verbal autopsy in a malaria-endemic region. Ann Trop Paediatr
14: 31–36.
21. Banerjee A, Moyo S, Salaniponi F, Harries A (1997) HIV testing and
tuberculosis treatment outcome in a rural district in Malawi. TransRSoc Trop
Med Hyg 91: 707–708.
22. Jahn A, Floyd S, Crampin AC, Mvula H, Mwinuka V, et al. (in press), Declining
child mortality despite HIV: results from demographic surveillance in northern
Malawi. Bull WHO.
Child Mortality and HIV
PLoS ONE | www.plosone.org8 June 2010 | Volume 5 | Issue 6 | e11320