Conceived and designed the experiments: TM KM RS SK. Performed the experiments: TM TW. Analyzed the data: TM GF. Contributed reagents/materials/analysis tools: GF. Wrote the paper: TM. Other: Contributed comments to the paper: GF SK RS KM TW. Gave advice on statistical analysis throughout the writing of the paper: GF.
The authors have declared that no competing interests exist.
It may be assumed that patterns of clinical malaria in children of similar age under the same level of exposure would follow a Poisson distribution with no over-dispersion. Longitudinal studies that have been conducted over many years suggest that some children may experience more episodes of clinical malaria than would be expected. The aim of this study was to identify this group of children and investigate possible causes for this increased susceptibility.
Using Poisson regression, we chose a group of children whom we designated as ‘more susceptible’ to malaria from 373 children under 10 years of age who were followed up for between 3 to 5 years from 1998–2003. About 21% of the children were categorized as ‘more susceptible’ and although they contributed only 23% of the person-time of follow-up, they experienced 55% of total clinical malaria episodes. Children that were parasite negative at all cross-sectional survey were less likely to belong to this group [AOR = 0.09, (95% CI: 0.14–0.61), p = 0.001].
The pattern of clinical malaria episodes follows a negative binomial distribution. Use of lack of a clinical malaria episode in a certain time period as endpoints for intervention or immunological studies may not adequately distinguish groups who are more or less immune. It may be useful in such studies, in addition to the usual endpoint of the time to first episode, to include end points which take into account the total number of clinical episodes experienced per child.
The standard immune-epidemiological approach to studying immunity in malaria uses the lack of an episode(s) of clinical malaria over a certain period of time as a marker of ‘being protected’
However, in Senegal, children followed up for five years were found to experience between 0–40 episodes of clinical malaria
Over distribution is common for many infectious organisms, for instance Woolhouse and colleagues
We examined a group of children that have been under malaria surveillance for between 3 to 5 years to investigate the pattern of susceptibility to clinical malaria over time and to identify factors associated with this increased susceptibility in an area of low-moderate malaria transmission in Kilifi District, Kenya.
Using total malaria episodes experienced per child while controlling for mean age and time at risk, it was found that the negative binomial regression fitted better than the Poisson regression (Likelihood ratio Chi-squared test = 229.12, p<0.001).
The X-axis is the total clinical episodes of malaria experienced per child and Y-axis is the proportion of children with given total disease episodes. The bars are the observed total number of cases per child, the black line is the predicted totals from the Poisson regression model, the dashed line represent the predicted total episodes from the negative binomial regression model while the crossed lines represents the predicted total episodes from the Pareto distribution. Figure (a) represents all the children, (b) all children under 5 years at the time the study started (who were followed up from 1998 to 2003) and (c) children ≥5 years of age (followed up from 1998 to 2001).
The dispersion parameter ‘k’ was 1.9 (95% CI: 2.4–1.5). When k→∞, then the data follows the Poisson distribution. The values obtained for ‘k’ suggest moderate over-dispersion of clinical malaria episodes suggesting that there are some children who are at increased risk of clinical malaria compared to others.
Children experiencing more episodes of clinical malaria appeared to be at greater risk of developing disease severe enough to require admission to hospital.
Box plot of the median(central line) 25%, 75% quartile ranges around the median (box width) and the upper and lower limits (T).
Poisson modelling was used to estimate the predicted number of clinical malaria episodes per child after controlling for mean age and time at risk. Children who experienced >2 episodes of clinical malaria above the total predicted were arbitrarily considered ‘more susceptible’. A total of 78 (21%) children comprised this ‘more susceptible’ group. Out of a total of 1,173 malaria episodes experienced during the period of the study, 55.3% was experienced by this ‘more susceptible’ group, representing only 23% of the cohort person years of follow-up.
Age | Group | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
Under 2 | Normal | 0.8 | 0.7 | 0.8 | 0.3 | 0.6 |
M. susceptible | 2.3 | 2.7 | 4.4 | 1.0 | 1.6 | |
2–4 yrs | Normal | 0.9 | 0.4 | 0.8 | 0.2 | 0.5 |
M. susceptible | 2.8 | 2.3 | 3.2 | 0.6 | 1.9 | |
≥5 yrs (ALL) | Normal | 0.6 | 0.4 | 0.4 | End of follow-up | |
M. susceptible | 2.6 | 1.6 | 2.2 | for these children |
Note:
Age in years at the start of the study
Year 1 = September 1998 to September 1999
Year 2 = October 1999 to October 2000
Year 3 = October 2000 to November 2001
Year 4 = November 2001 to November 2002
Year 5 = December 2002 to September 2003.
M. susceptible = ‘more susceptible’. These are the children who experienced >2 episodes of clinical malaria above the total predicted from the Poisson regression model.
Factor | Normal | ‘More Susceptible’ | Crude Odds ratio | ||
N = 295 (79.1%) | N = 78 (20.9%) | (95% confidence intervals) | |||
Female | 143 (48.5%) | 31 (40%) | 1.42 (0.86–2.37), p = 0.2 | ||
Bednets (N = 342) | 140 (52.6%) | 40 (52.6%) | 1.0 (0.6–1.7), p = 1.0 | ||
Transmission | 105 (35.6%) | 38 (48.7%) | 1.71 (1.03–2.85), p = 0.03 | ||
Parasitological cross-sectional survey (n = 238). | |||||
>5,000 par/µl of blood | 13 (7.3%) | 13 (22%) | 3.6 (1.5–8.48), p = 0.002 | ||
Always –ve | 70 (39.1%) | 7 (11.9%) | 0.21 (0.08–0.5), p<0.001 | ||
Genetic markers | |||||
Sickle trait (N = 352) | 36 (13%) | 4 (5.4%) | 0.38 (0.13–1.12), p = 0.07 | ||
Thalassaemia (N = 284) | 148 (67.9%) | 47 (71.2%) | 1.17 (0.6–2.14), p = 0.6 | ||
Incidence of clinical disease [Episodes/child/year and 95% confidence intervals] | |||||
Normal | ‘More Susceptible’ | ||||
Malaria fevers | 0.56 (0.51–0.61) | 2.35 (2.17–2.53) | |||
Non-malarial fevers | 0.91 (0.85–0.97) | 0.93 (0.81–1.04) |
Note:
Bednets either untreated or treated that were in good condition.
*Transmission: This reflects the household level of transmission and shows the proportion of children in the two groups that came from homes with above average parasite rate (≥50%) compared to those below average (<50%).
These are geometric mean parasite densities in those cross-sectional surveys were the slide was positive. The cut-off for high geometric mean parasite density was set arbitrarily at >5,000 parasites/µl of blood compared to those with less
Compares those who were always negative at all six cross-sectional surveys with those who were positive at least once.
α Thalassaemia genotype: Homozygous (-α/-α) and heterozygous (αα/-α) compared to normal (αα/αα). Comparing homozygous and heterozygous alone did not make a difference to these associations.
The children in the ‘more susceptible’ group were also 4.9 times more likely to be admitted to hospital for malaria than others [Crude or Unadjusted Odds Ratio (UOR) = 4.9 (95% CI: 2.6–9.3), p<0.001).
Univariate analysis (Crude odds ratio) was used to look at associations between each individual variable and susceptibility (
Among those that were parasite positive in the parasitological surveys, children that had high geometric mean parasite density (GMPD) (>5,000 parasites/µl of blood) were more likely to be ‘more susceptible’ to malaria [UOR = 4.2 (95% CI: 1.8–9.7), p<0.001]. The incidence of clinical malaria (episodes/child/year) among those slide negative at all cross-sectional surveys [0.69 (95% CI: 0.6–0.79)] was lower than that of those who had a GMPD at the cross-sectional surveys of <5,000 parasites/µl of blood [1.19 (95% CI: 1.1–1.3)] which was lower than that of those with a GMPD ≥5,000 parasites/µl of blood [1.78 (95% CI: 1.52–2.03)].
Sickle cell trait was associated with decreased risk of being in the ‘more susceptible’ group but this was not statistically significant [UOR = 0.38 (95% CI: 0.13–1.12), p = 0.07] while children from households with average parasite rate ≥50% (which indicated high exposure to malaria) were more likely to be in the ‘more susceptible’ group [UOR = 1.71 (95% CI: 1.03–2.85), p = 0.03].
Using Poisson regression and controlling for sickle trait, high geometric mean parasite density, household level of transmission as well as the clustering effect of household, there was evidence that children who were parasite negative in all six cross-sectional surveys were less likely to belong in the ‘more susceptible’ group [Adjusted Odds ratio (AOR) = 0.29 (95% CI: 0.14–0.61), p = 0.001)]. None of the other factors (including household level of transmission) showed statistically significant associations in this multiple regression.
Using the random effects Poisson regression likelihood ratio test, there was evidence, of clustering of episodes of clinical malaria within households (p = 0.03). However, the number of households was small (n = 60) with large differences in the numbers of children per house (6.2±4.1). Half of the households did not have a single ‘more susceptible’ child but none of the households were made up purely of ‘more susceptible’ children (
The clear circles represents the households that did not have any children ‘more susceptible’ than others, the circles with squares represent the households that had between 1–3 children ‘more susceptible’, the grey circles represent those that had 4–6 and the black circles represent those that had more than 7 children in the households ‘more susceptible’ than others. The smaller dots spread across the map are all the other households within the larger study area that were not included in the surveillance.
Data collected in one malaria season (4 months from May to August of 1999) was analysed to find out whether it was possible to identify this high risk group over a short period of time. Within this 4-month period, 78% of the children in the ‘more susceptible’ group experienced at least one episode of clinical malaria while only 29% of the others experienced an attack. Five children (4 from the ‘more susceptible’ group) had 3 episodes of malaria within the four months of surveillance.
Dashed line represents the ‘more susceptible’ children and the solid line represents the time to first episode for the other children.
A total of 77 (21%) children did not experience a single episode of clinical malaria over several years of follow-up. In this group, 23% had sickle cell trait, 49% slept under an intact bednet, 59% were over five years of age and 36% lived in houses with transmission above average (compared to 9%, 53%, 36% and 39% respectively in the others who experienced any number of clinical malaria attacks). Figure one illustrates that there were more children observed to have no clinical malaria episodes than would be expected if the data were to fit a Poisson distribution especially among those under 5 years of age.
We found that within a small geographical area and over several years, the risk of experiencing clinical episodes of malaria was markedly heterogenous, some children being ‘more susceptible’ than others of the same age group. This increased susceptibility appears to be malaria specific as it was not observed for non-malaria fevers.
The pattern of clinical malaria attacks followed a negative binomial distribution. For the purposes of further analysis, we arbitrarily chose children who over the study period experienced a total of more than two episodes of clinical malaria above what was expected from the Poisson distribution. This group designated as ‘more susceptible’ were also at higher risk of clinical malaria severe enough to warrant admission to hospital and some were admitted more than once. This is consistent with earlier work by Snow and colleagues
We also noted that there were a large number of children who do not get any malaria episodes in the whole period of follow-up. This is a phenomenon that was also observed from 11 years of malaria surveillance in an area in the Sudan with low malaria transmission
Children who were parasite negative at all cross-sectional surveys were less likely to belong to the ‘more susceptible’ group of children. Whilst this may seem obvious, it is important to recognise that being repeatedly negative may stem from different circumstances. Some children may be less exposed (some homes did not have a single ‘more susceptible’ child), in which case they may in fact have increased susceptibility that is not manifest due to lack of exposure to infected bites. However, the effect of local variations in exposure (measured by household parasite rates) was weak in the multiple regression model though there was evidence of spatial clustering of clinical malaria attacks. Alternatively, some children could be parasite negative in the face of continuous exposure, in which case they have genuine reduced susceptibility. Such protection could stem from a range of factors including behavioural differences between households and genetic factors which either act directly to control the risk of clinical disease or by influencing immune responses. Sickle cell trait probably provides the greatest degree of protection of any single genetic polymorphism. In this study, the odds ratio for association with the less susceptible group was consistent with this, though not statistically significant. However, it should be noted that we have previously estimated in the same group of children that sickle cell trait contributes only 2.1% of the variance in incidence of non-severe clinical malaria
Children with low parasite densities at cross sectional survey (i.e. lower geometric mean densities over time) had a lower incidence of clinical malaria compared to those with higher densities, suggesting that ‘resting’ levels of asymptomatic parasitaemia may reflect the immune status of the individual. Although the “strain specific” aspects of immunity to malaria are often emphasised, such an observation would argue for a significant degree of cross protective immunity. For instance, in a study in Senegal on ten children who experienced more episodes than expected over a short time period, it was found that each new clinical episode was caused by a distinct genotype
Whatever the underlying mechanisms, the phenomenon of increased susceptibility was evident over even short periods of time: within a 4-month period in a single malaria season, 78% of the ‘more susceptible’ group (as defined by their experience over the entire period of study) of children experienced at least one episode of clinical malaria compared with 29% in the rest of the population. However, depending on the relative size of the two groups, it may be difficult to tell these groups of children apart over a short time period. The significance of this for longitudinal studies may vary depending on the reasons for the heterogeneity observed and the questions being asked. Whatever the case, it is important to be aware that simply detecting a clinical episode of malaria in a time limited longitudinal study may not identify groups with homogenous levels of ‘protection’ and ‘susceptibility’.
It has previously been suggested that concentrating control resources on the ‘more susceptible’ children may lead to more successful control interventions
In conclusion, we have confirmed over-dispersion in the distribution of clinical malaria episodes in a relatively small area. Although this may have possible implications for targeting interventions, we suggest that such heterogeneity is especially relevant to studies of both naturally acquired immunity and vaccine trials. It may be useful in such studies, in addition to the usual endpoint of the time to first episode, to include end points which take into account the total number of clinical episodes experienced per child.
The study was conducted as part of a longitudinal study defining and describing non-severe malaria among people living in Ngerenya, an area of low-moderate malaria transmission in Kilifi District on the coast of Kenya. Consent was sought from parents of 373 children <10 years of age in August-September 1998. Verbal consent was sought from village leaders and heads of households. The study was then explained in the local language to the mother or child's direct care giver. Information sheets were left with the family for discussion and the study explained again the following day and any questions answered. The mother or child's direct care giver was then asked to sign or thumb-print on a consent form to show willingness for her child/children to participate in the study after fully understanding it. They were also made aware that they could withdraw their child/children from the study at any point.
Details of the weekly surveillance, and parasitological surveys are described in Mwangi
The primary measure for analysis was episodes of clinical malaria among study participants during follow-up. Clinical malaria was defined as fever and any level of parasitaemia for children under one year old and fever accompanied by parasitaemia ≥2,500 parasites/µl of blood for children 1–10 years old
Six parasitological surveys were conducted, half in the high transmission, wet season (June 1999, July 2000, June 2001) and half in the dry season when transmission was low (March 2000, October 2000, March 2001). Blood samples were taken for genetic and immunology studies. A study to investigate bednet condition and use was conducted in June 2000, details of which are described elsewhere
Haemoglobin types (HbA, HbS) were characterized by electrophoresis using cellulose acetate gels (Helena, USA), while participants were typed for the common African 3.7-kb α-globin deletion by polymerise chain reaction
Admission data were collected at the paediatric wards at the Kilifi District Hospital (KDH) which is the main referral hospital for residence of the study area and would capture most admissions. All paediatric admissions to the KDH were assessed by a clinician and a standard set of laboratory parameters collected routinely
All records were double entered into a database (FoxPro® version 2.5) and both entries cross-checked for errors before cleaning. All data analyses were done using STATA® software, version 9.0. (Stata Corporation, Texas, USA). We generated a map using ArcGIS® version 9.0 plotting all households within the study and indicated the total number of “more susceptible” children within each study household.
Kruskal-wallis test was used to investigate the difference in the median total number of malaria episodes experienced per child among those admitted and those not admitted with malaria. Poisson regression models, negative binomial regression models and Pareto distribution were compared for fit using total episodes of clinical malaria per child for the period of follow-up as the outcome measure. These distributions and their plots were generated using the R statistical package, version 2.62 (R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
A Poisson model was used to estimate the predicted total episodes of clinical malaria per child after controlling for mean age and time at risk. Children who experienced a total of >2 episodes of clinical malaria above what was predicted for the time at risk were considered within this analysis as ‘more susceptible’. After identifying the ‘more susceptible’ group, we sought to investigate factors related to this increased susceptibility. Crude odds ratios were used to identify these factors. Factors with a p-value <0.1 were put in a multiple Poisson regression model in order to estimate the same associations after controlling for possible confounders.
In order to investigate the presence of clustering of ‘more susceptible’ children within the households, we used the random effects Poisson regression model.
Kaplan-Meier survival curves were plotted using the time in weeks to the first episode of clinical malaria in comparing the ‘more susceptible’ group to the others.
To determine the level of malaria transmission in an individual house, parasite prevalence data from a single cross-sectional survey conducted in August-September of 1998 were used. The overall mean parasite rate among those 1–9 years of age was 43.5% (95% CI: 38–49.1%). Due to the small number of children per house in some of the homes, houses were merged into 13 zones (<2 km apart) and the parasite rate calculated. Households were then classified as above average (parasite rate ≥50%) and those average or below (parasite rate <50%).
We wish to thank all the clinical staff of the KEMRI Unit at Kilifi who made this study possible. A special acknowledgement for the dedication of the fieldworkers who carried out all the surveys, Monica Odhiambo for data entry, Christopher Nyundo and Lazarus Mramba for all the help with the map and R plots. We would also like to thank Margaret Mackinnon and Phillip Bejon for extremely helpful comments as the manuscript developed. This paper is published with the permission of the director of KEMRI.