The temporal and spatial distribution of malaria in Africa, with emphasis on southern Africa

235 Pages·2009·4.85 MB·English

The temporal and spatial distribution
of malaria in Africa,
with emphasis on southern Africa
INAUGURAL-DISSERTATION
zur
Erlangung der Würde eines Doktors der Philosophie
vorgelegt der
Philosophisch-Naturwissenschaftlichen Fakultät der
Universität Basel
von
Marlies H. Craig
aus
Durban, Südafrika
Südafrika, 2009
Genehmigt von der Philosophisch-Naturwissenschaftlichen Fakultät auf Antrag von Prof. Dr. M.
Tanner, Prof. Dr. T. Smith, Dr. J. Cox.
Basel, den 11. Dezember 2007
Prof. Dr. Hans-Peter Hauri
Dekan
To:
Andrew,
faithful husband, friend and brother;
Miriam, Philip and Simon,
who light up my life;
and Brian Sharp,
sorely missed,
leader, mentor and friend.

v
Table of Contents
Page
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxvii
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2: A climate-based distribution model of malaria transmission in sub-Saharan
Africa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Chapter 3: Developing a spatial-statistical model and map of historical malaria prevalence in
Botswana using a staged variable selection procedure . . . . . . . . . . . . . . . . . . . . 37
Chapter 4: Time-space analysis of malaria prevalence data in Botswana . . . . . . . . . . . . . . . 69
Chapter 5: Exploring thirty years of malaria case data in KwaZulu-Natal, South Africa,
Part I: the impact of climatic factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Chapter 6: Exploring thirty years of malaria case data in KwaZulu-Natal, South Africa,
Part II: the impact of non-climatic factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Chapter 7: Spatial and temporal variation in malaria incidence in South Africa . . . . . . . . 131
Chapter 8: Discussion and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
vi Acknowledgements
Acknowledgements
First, I would like to thank my supervisor Prof. Tom Smith for his support during the years
that this thesis was in progress, for his professional guidance, scientific insights and generous
help, particularly on my last working trip to the STI. I consider myself very fortunate to have
benefited from his expertise. Many thanks to him and his wife Julie for hosting me so kindly
in Basel on more than one occasion. Likewise I sincerely appreciate the kind efforts of Dr
Immo Kleinschmidt and Dr Penelope Vounatsou, who patiently taught me so much of what I
know about statistics. Most of the statistical work in this thesis would have been quite
impossible without their guidance. Sincere thanks to Dr Musa Mabaso, helpful critic of
numerous manuscripts and co-worker in the MARA project. It is always a pleasure working
with him. Thanks Musa, we have come a long way together.
I sincerely wish to acknowledge Dr Brian Sharp, recently deceased. I am extremely grateful
for the privilege of having experienced his leadership. He was and remains a role model to
me, a mentor, a guide. With sound motives and the bigger picture in mind, he was neither
petty nor negative, but always saw the potential in people and situations. He did all in his
power to support working mothers. I valued his opinion enormously and miss him very much.
I would also like to acknowledge Dr David le Sueur, also deceased, who gave me valuable
guidance during the early days of my career. He set me on a course of modelling malaria
distribution, by sending me for training in Idrisi in the USA, guiding the development of the
malaria distribution model, and then employing me as GIS coordinator in the MARA project.
Sincere thanks go to the other MARA collaborators, in particular Prof. Christian Lengeler and
Prof. Don de Savigny, for their availability, support and commitment from the start of the
Acknowledgements vii
project until today. They were a major source of practical help, good advice and inspiration,
and remain valued friends and allies. Prof. Lengeler was also the principal instigator who
urged me to consider registering for a PhD through the STI. Andrew’s chocolate factory didn’t
quite pan out, but the rest did. Thank you also for hosting me in Basel so kindly.
My gratitude also goes to Prof. Marcel Tanner for his enthusiastic support, and for giving me
the opportunity of enrolling for doctoral studies through the STI, and to the STI and the
Rudolf Geigy Stiftung zu Gunsten des Schweizerischen Tropeninstituts for supporting this
study financially. I also value the support of the South African Medical Research Council,
which grants its employees extended leave of absence so that we may pursue further study
overseas.
I greatly appreciate the many other colleagues and fellow-students, both at the Medical
Research Council and the Swiss Tropical Institute, in particular Laura and Dominic Gosoniu,
Amanda Ross, Nicholas Maire, Claudia Sauerborn, Sohini Banerjee, for, above all, your
friendship, for many stimulating discussions (not always about work) and for all the good
times. I never could have done this work in isolation.
Thank you Andrew for your loving support and encouragement, especially when I felt like
giving up. Thank you for accompanying me to Switzerland for 10 months, and for running the
show back home during various shorter trips to Basel. Thank you Miriam and Philip for
looking after dad when I was away. You are fantastic kids.
Sincere thanks to my aunt Marianne Kassier who kindly translated the summary into German,
and to Michael Bretscher for editing the “Fachdeutsch”. I really appreciate your help. Finally,
many thanks to Dr Jon Cox for agreeing to be the external examiner.
viii Summary
Summary
The three-way relationship between the Plasmodium parasite, the Anopheles mosquito vector
and the human host determines the incidence of malaria disease. The three life cycles, the
interactions respectively between human and parasite, human and mosquito, and mosquito and
parasite, and the ultimate transmission cycle, vary in time and space. Environmental, genetic
and behavioural factors influence the three life cycles and the interactions. These factors also
vary in time and space. At every level the variation itself, whether random or cyclical, is not
uniform but varies in frequency and magnitude. Explaining, and particularly predicting,
malaria transmission rates in time and space thus becomes a difficult undertaking.
Knowing and understanding some of this variation, and its causes, is important for well-timed
and well-targeted malaria interventions. In the fringe areas of malaria in Africa, which are
prone to epidemics, some forewarning of unusually high incidence periods would be valuable
to malaria control and management services.
This thesis investigated the temporal and spatial effects on malaria transmission of various
environmental factors, particularly climate, and of non-climatic factors, particularly those
relating to malaria control. Different data sets and methodological approaches were applied in
seven separate studies, and malaria distribution in time and space was investigated at different
scales.
At the continental scale, the distribution of malaria in Africa was modelled as a factor of
climate using raster GIS techniques.
Summary ix
At the national scale, using prevalence data from Botswana, spatial variation in prevalence
was modelled as a factor of environmental determinants, prior to comprehensive malaria
control. The spatial and inter-annual variation in prevalence, in the presence of intense
control, was also modelled as a factor of climate.
At the sub-national level South Africa was used as an example. Inter-annual variation in
malaria incidence in the highest-risk province was explored for possible links with climatic
and non-climatic factors. Finally, inter-annual and spatial variation in sub-provincial level
incidence data for South Africa, were analysed with respect to climatic and non-climatic
determinants, for which data were available.
The two study areas (Botswana and South Africa) both lie at the fringe of malaria distribution,
experience strongly seasonal transmission and epidemics, and both benefit from intensive
malaria control. The two study areas represent two slightly different scenarios: in Botswana
the analysis period covered the steady introduction of comprehensive control, while in South
Africa the study period covered a time when effective control was being threatened by the
spread of insecticide- and drug resistance, and the general health of the population was
increasingly affected by the HIV pandemic.
The main findings were the following:
• It was possible to estimate the distribution of malaria in Africa fairly successfully from
long term mean climate data via simple GIS methods. The model compared well with
contemporary malaria data and historical ‘expert opinion’ maps, excepting small-scale
ecological anomalies. The model provided a numerical basis for further refinement
and prediction of the impact of climate change on transmission. Together with
x Summary
population, morbidity and mortality data, it has provided a fundamental tool for
strategic control of malaria.
• In Botswana the spatial variation in childhood malaria prevalence, prior to intense
comprehensive control, was significantly associated with underlying environmental
factors. It could be predicted and mapped using only three environmental predictors,
namely summer rainfall, mean annual temperature and altitude. After starting with a
long list of candidate variables, this parsimonious model was achieved by applying a
systematic and repeatable staged variable exclusion procedure that included a spatial
analysis. All this was accomplished using general-purpose statistical software.
• In the presence of intense control, the spatial and temporal variability in childhood
malaria prevalence in Botswana could no longer be explained by variation in climate.
The effects of malaria control and good access to treatment seem to have replaced
climate as the main determinant of prevalence. This also suggests that prevalence, a
less direct measure of transmission rate, is more prone to non-climatic effects than
incidence rate.
• Total population malaria incidence in KwaZulu-Natal, the highest risk province of
South Africa, remained significantly influenced by climate over a 30 year period, even
in the presence of intense control. The inter-annual variation in case numbers were
significantly associated with several climate variables, mainly mean annual daily
temperatures and summer rainfall. However, climate factors did not explain the longer
term total incidence rates.
• The longer term trends in total malaria incidence in KwaZulu-Natal province, over the
same 30 years period, were significantly associated with the spread of anti-malarial
drug resistance and HIV prevalence. Cross-border movements of people, agricultural
activities and emergence of insecticide resistance also affected the level of malaria