Spatial Analysis and Mapping of Malaria in Limpopo Province, South Africa

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Spatial Analysis and Mapping of Malaria in Limpopo Province, South Africa

Natashia Morris

Dissertation submitted in partial fulfilment of requirements for the degree of Master of Science (Geographical Information Science & Systems) UNIGIS Distance Learning Programme, Center for Geoinformatics,

Salzburg University, Austria August 2012

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N Morris 2012 UNIGIS MSc Dissertation Page 2 of 100

Disclaimer

The results presented within this thesis are based on my own research undertaken within the Malaria Research Unit of the South African Medical Research Council. All assistance and support received from individuals and organisations is acknowledged and full reference is made to all published and unpublished sources consulted.

This dissertation has not been previously submitted for a degree at any institution.

Natashia Morris

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Abstract

Border areas of South Africa in which malaria transmission is concentrated have experienced falling case numbers over the last decade with the expansion of a regional control initiative into Mozambique and Swaziland. However, malaria cases in Limpopo Province persist in relatively high numbers, and distribution patterns appear unchanged during that time. Analysis of malaria case incidence over the last decade and a half was conducted using global and local spatial statistics to provide insights into the patterns of distribution of the disease both spatially and temporally. Findings were used to explore the appropriate targeting of interventions in the campaign to push back the frontier of malaria and to achieve elimination both in the province and in the country at large.

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Acknowledgements

I wish to record my sincere gratitude to: Professor Rajendra Maharaj, Director of the Malaria Research Unit (MRU) of the Medical Research Council(MRC), for guidance and support through the course of this study; Mr Philip Kruger, Manager of the Limpopo Province Malaria Control Programme, for permission to access the data and for invaluable insights into the dynamics of malaria distribution and control in the province; Mr Ndabezitha Shezi and Mr Nkululeko Bhengu of the Health GIS Centre of the MRC for their tireless efforts with georeferencing that made this study possible; Mr Ishen Seocharan, Manager of the Database Department of the MRU, for assistance with data preparation; Ms Tarylee Reddy of the Biostatistics Unit of the MRC for advice on the statistical analysis; Dr Jaishree Raman, senior molecular biologist at the MRU, for contributions to the study proposal; the MRC for financial support; Ms Ann Olivier, South African Study Centre Administrator at UNIGIS International, for guidance; Mr Anthony Morris for assistance with editing; the late Dr Brian Sharp who remains a constant inspiration; and finally, my husband, Gareth, and daughter, Abigail, for their love and support.

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List of Tables

Table 1: Malaria incidence and deaths by season in Limpopo Province (1999/2000 to 2010/2011 malaria seasons) ... 33 Table 2: Malaria cases per month per season in Limpopo Province (1999/2000 to 2010/2011 malaria seasons) ... 35 Table 3: Malaria incidence and deaths by districts of Limpopo Province (1999/2000 to 2010/2011 malaria seasons) ... 36 Table 4: Incidence of malaria cases per 100,000 person years per municipality per malaria season, Limpopo Province (1999/2000 to 2010/2011) ... 37 Table 5: Malaria cases by source of infection per malaria season, Limpopo Province (1999/2000 to 2010/2011) (note that hyphens denote no cases reported) ... 51 Table 6: Sum and proportion of local cases per district per malaria season, Limpopo Province (1999/2000 to 2010/2011 malaria seasons) ... 51 Table 7: Number of localities reporting malaria and maximum and mean cases

reported per locality per season in Vhembe and Mopani Districts (1999/2000 to

2010/2011) ... 58 Table 8: Average nearest neighbour summary for localities and individual cases ... 59 Table 9: Indices and test results of clustering of high and low values using the Getis-Ord General G statistic for Vhembe and Mopani Districts per malaria season (1999/2000 to 2010/2011) ... 60 Table 10: Spatial autocorrelation indices and test results using the Moran's I statistic for Vhembe and Mopani Districts per malaria season (1999/2000 to 2010/2011) ... 61 Table 11: Observed mean distance to nearest neighbour, maximum distance to include one neighbour and resulting threshold distance for hot spot analysis for Vhembe and Mopani Districts by malaria season (1999/2000 to 2010/2011) ... 66 Table 12: Malaria hot spot localities identified in Vhembe and Mopani Districts with malaria cases greater than 5 and z-scores of 1.96 and above in all seasons from

2005/2006 to 2010/2011 ... 71 Table 13: Localities reporting 20 or more malaria cases in all seasons between

2005/2006 and 2010/2011 (z-scores of 2.58 and above denoting hot spots are

accented) ... 71 Table 14: Number of structures sprayed per season per district in Limpopo Province (2005/2006 to 2010/2011 malaria seasons) ... 75

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List of Figures

Figure 1: Map of global malaria risk (Hay and Snow, 2006) ... 12 Figure 2: Official map of risk of malaria infection in South Africa (Medical Research Council, 2008) http://www.malaria.org.za/Malaria_Risk/Risk_Maps/risk_maps.htm . 14 Figure 3: Life cycle of the malaria parasite (NIAID, 2012)

http://www.niaid.nih.gov/topics/malaria/pages/lifecycle.aspx ... 15 Figure 4: Malarious provinces of South Africa and endemic districts of Limpopo

Province ... 20 Figure 5: Process of passive notification of malaria cases in South Africa... 24 Figure 6: Process of investigation of passively notified malaria cases in South Africa .. 25 Figure 7: Process of IRS and related reporting in South Africa ... 25 Figure 8: Cases and deaths by malaria season in Limpopo Province (1999/2000 to 2010/2011) ... 34 Figure 9: Box plot of mean malaria cases per municipality per malaria season ... 34 Figure 10: Malaria cases per district per season, Limpopo Province (1999/2000 to 2010/2011 malaria seasons) ... 36 Figure 11: Malaria cases, incidence (per 100,000 person years) and deaths in Mopani and Vhembe Districts by malaria season (1999/2000 to 2010/2011) ... 37 Figure 12: Malaria cases by residential locality of patients by season, Limpopo Province (1999/2000 to 2004/2005) ... 39 Figure 13: Malaria cases by residential locality of patients by season, Limpopo Province (2005/2006 to 2010/2011) ... 40 Figure 14: Malaria cases by residential locality of patients by month (January to June), Limpopo Province (aggregate for 1999/2000 to 2010/2011 malaria seasons) ... 41 Figure 15: Malaria cases by residential locality of patients by month (July to

December), Limpopo Province (aggregate for 1999/2000 to 2010/2011 malaria

seasons) ... 42 Figure 16: Malaria incidence per 100,000 person years per residential municipality of patients per season, Limpopo Province (1999/2000 to 2004/2005) ... 43 Figure 17: Malaria incidence per 100,000 person years per residential municipality of patients per season, Limpopo Province (2005/2006 to 2010/2011) ... 44

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Figure 18: Kernel density estimation based on cases per residential locality of patients per season, Limpopo Province (1999/2000 to 2004/2005) ... 46 Figure 19: Kernel density estimation based on cases per residential locality of patients per season, Limpopo Province (2005/2006 to 2010/2011) ... 47 Figure 20: Directional distribution of individual malaria cases by season and by month by season for all districts (red and orange) and for Vhembe and Mopani Districts (dark and light green) (1999/2000 to 2010/2011 malaria seasons) ... 49 Figure 21: Source of infection reported by locality for selected seasons, Limpopo

Province (1999/2000, 2008/2009, 2009/2010 and 2010/2011) ... 52 Figure 22: Statistically significant high value clusters, low value clusters and spatial outliers by locality by season for Vhembe and Mopani using Anselin Local Moran's I statistic (1999/2000 to 2004/2005) ... 63 Figure 23: Statistically significant high value clusters, low value clusters and spatial outliers by locality by season for Vhembe and Mopani using Anselin Local Moran's I statistic (2005/2006 to 2010/2011) ... 64 Figure 24: Graphs of z-scores generated incrementally at specified distances using spatial autocorrelation for all cases for Vhembe and Mopani Districts for the malaria seasons 1999/2000 to 2010/2011; peak values were specified as threshold distances during hot spot analysis ... 68 Figure 25: Hot spot analysis of malaria cases by locality by season for Vhembe and Mopani Districts (1999/2000 to 2004/2005) ... 69 Figure 26: Hot spot analysis of malaria cases by locality by season for Vhembe and Mopani Districts (2005/2006 to 2010/2011) ... 70 Figure 27: Number of structures sprayed per malaria planning sector, Limpopo

Province (2005/2006 to 2010/2011 malaria seasons) ... 76 Figure 28: Entomological surveillance sites in Limpopo Province at which mosquito vectors of the complex Anopheles gambiae were identified between 2006 and 2007 77 Figure 29: Numbers of structures sprayed per locality per malaria season in relation to malaria hot spots (2005/2006 to 2010/2011) ... 79 Figure 30: National form for the notification of medical conditions (GW17/5) ... 90 Figure 31: Provincial malaria case investigation report (EP6) ... 91 Figure 32: Form used for record of daily spray activities of individual spray officers ... 99 Figure 33: Form used for reporting summaries of spray activity records for an entire IRS team ... 100

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Abbreviations

ACT Artemisinin-based combination therapy EPR Epidemic preparedness and response

GFATM The Global Fund to Fight AIDS, Tuberculosis and Malaria IRS Indoor residual spraying

ITN Insecticide treated bed net KDE Kernel density estimation

LSDI Lubombo Spatial Development Initiative MCP Malaria Control Programme

MIS Malaria Information System

MRC South African Medical Research Council MRU Malaria Research Unit of the MRC

NICD National Institute of Communicable Diseases PCR Polymerase chain reaction

PMI President’s Malaria Initiative RBM Roll Back Malaria Initiative RDT Rapid diagnostic test

SIS Spraying Information System VCRU Vector Control Reference Unit WHO World Health Organisation

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1 Introduction

1.1 Global malaria situation

Malaria is an infectious disease of the tropics and sub-tropics and is concentrated in Sub-Saharan Africa, Central and South-East Asia, Oceania and Central America (Figure 1).

According to the World Malaria Report of 2011, an estimated 216 million malaria cases and 655 000 malaria deaths were reported globally in 2010. Of these, an estimated 174 million malaria cases, accounting for an overwhelming 81% of all cases globally, and 596 000 malaria deaths occurred on the African continent. The African incidence of malaria per 1 000 population at risk was reported at around 246 in 2010 compared with the world average of 65. Approximately 80% of malaria in Africa was noted to occur in children under the age of five, with 90% of all deaths recorded annually occurring amongst this high risk age group (WHO, 2011).

Global declines of 17% and 26% in malaria incidence and mortality rates respectively since 2000 are lower than agreed upon reductions of 50% on both counts (WHO, 2011).

Figure 1: Map of global malaria risk (Hay and Snow, 2006)

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1.2 Malaria in South Africa

Malaria in South Africa is endemic in eastern Mpumalanga and in north-eastern parts of the provinces of Limpopo and KwaZulu Natal (Figure 2). High numbers of imported cases are reported in Gauteng Province, and relatively small numbers in the other five provinces. The disease is highly seasonal in the country with the risk of infection greatest during the wet summer months. Malaria cases typically begin to rise in December and January, peaking in April or May and then declining over the subsequent months (Sharp et al, 1988).

Of an estimated national population of 50 132 817 in 2010, some 4% were resident in high malaria transmission areas with an incidence of one or more malaria cases per thousand population and around 6% in low transmission areas with an incidence of less than one case per thousand persons at risk (WHO, 2011). A total of 8 060 confirmed malaria cases and 83 malaria deaths were reported nationally in 2010.

While much has been published in academic literature over the last decade with respect to malaria in southern Africa, few have reported on the national epidemiology of South Africa in recent years. Maharaj (1995) reported the national situation between 1980 and 1994 and Craig et al (2004) investigated the impact of climate on malaria case numbers in the Province of KwaZulu Natal between 1920 and 2000.

More recently, Gerritsen et al (2008) reported on malaria incidence in Limpopo Province between 1998 and 2007, and Ngomane and de Jager (2012) described changes in malaria morbidity and mortality in Mpumalanga Province between 2001 and 2009.

1.3 Malaria transmission

Malaria is caused by the Plasmodium genus of parasite, and the four species responsible for malaria in humans are Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale, the former two of these being the most common.

The major species of parasite accounting for more than 85% of all confirmed cases in South Africa is Plasmodium falciparum, with Plasmodium vivax also occurring (Maharaj, 1995).

The malaria parasite is transmitted to humans by at least 11 different species of the Anopheles genus. The major species of vector in South Africa are Anopheles arabiensis and Anopheles funestus. All Anopheles mosquitoes breed in water and all of the important vector species are known to bite at night.

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Figure 2: Official map of risk of malaria infection in South Africa (Medical Research Council, 2008)

The intensity of transmission is impacted upon by climatic conditions including primarily temperature, rainfall and humidity. In areas where transmission is seasonal, epidemics can occur during and after the rainy season.

Human immunity to the parasite is known to develop with prolonged periods of exposure amongst populations resident in areas of moderate or intense transmission.

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1.4 Life cycle of the malaria parasite

The life cycle of the malaria parasite is complex and involves two hosts, human and mosquito (Jones and Good, 2006). The Anopheles mosquito injects the sporozoite form of the Plasmodium parasite along with its saliva into the blood stream of humans (Figure 3).

Sporozoites enter the liver of the human host where they multiply for between five and seven days (Wellcome Trust, 2006). Thousands of merozoites are formed during this time which are then released from the liver and into the blood to invade red blood cells and multiply. Infection spreads to new cells when the red blood cells burst and the merozoites are released.

Some of the merozoites develop into the sexual stage of the parasite known as gametocytes (Wellcome Trust, 2006) which are then taken up by the Anopheles mosquito when it feeds on human blood, termed a blood meal (Vaughn et al, 2008).

Figure 3: Life cycle of the malaria parasite

(National Institute of Allergy and Infectious Diseases, 2012)

The life cycle of the parasite continues within the mosquito’s gut where gametocytes develop further into gametes which then bind together to form zygotes. These zygotes invade the wall of the mid-gut of the mosquito and go on to develop into oocysts which grow, burst and release sporozoites.

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The sporozoites travel to the salivary glands of the mosquito and are then passed back to human hosts when bitten by the Anopheles mosquito thus perpetuating the life cycle of the parasite.

1.5 Symptoms of malaria

Malaria is typically characterised by fever, headaches, chills and vomiting. Untreated within 24 hours, the infection can progress to stages of severe illness. Severe malaria in children has been linked to anaemia, respiratory failure and cerebral malaria. In the case of infection with Plasmodium vivax or Plasmodium ovale, relapses may occur months after the initial infection. Population groups at higher risk of infection include children under the age of five, pregnant women and those with reduced immunity as the result of infection with the HIV/AIDS virus.

1.6 Malaria treatment and prevention

Malaria can be prevented both through the use of antimalarial drugs and through non- medicinal measures primarily involving avoidance of bites by mosquitos. In the event of the disease being contracted, awareness of the symptoms associated with infection and early diagnosis and treatment are critical to recovery (Maharaj, 2001).

Mefloquine, doxycycline and atovaquone-proguanial are recommended as the three effective chemo-prophylactic options for prevention of malaria in the event that malaria risk is sufficient to warrant protection with drugs (South African National Department of Health, 2009).

In South Africa, Artemether-lumefantrine, Quinine with Clindamycin and Quinine with Doxycycline are indicated as the first line treatment for infection with Plasmodium falciparum, with Artesunate and Quinine indicated in the event of first line treatment failure (South African National Department of Health, 2010). Quinine and, more recently, IV Artesunate, are used to treat severe malaria, and Artemether-lumefantrine with Primaquine or Cloroquine with Primaquine are used to treat infection with Plasmodium vivax.

1.7 Malaria control

Malaria control strategies chiefly involve the use of appropriate interventions to prevent malaria transmission coupled with proper case management with respect to

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diagnosis and treatment. Progress in malaria control, treatment and prevention has been hampered by the rise of resistance to commonly used drug classes and to the pyrethroid class of insecticides widely used for Indoor Residual Spraying (IRS) and for the preparation of insecticide treated bed nets (ITNs) (WHO, 2011).

South Africa is situated on the southern fringe of malaria distribution on the African continent. IRS forms the backbone of the national control strategy. A combination of liquid pyrethrum and kerosene was first used as an indoor spray in KwaZulu Natal in 1932, and was replaced with DDT in 1946 (Sharp and le Sueur, 1996). With international pressure for the removal of DDT, the pyrethroid Deltamethrin was introduced in 1996 (Mabaso, 2004).

Following the epidemic of 1999, attributed in large part to mounting pyrethroid resistance amongst the local vector population and during which case numbers in northern KwaZulu Natal exceeded some 40 000, several interventions were put in place (Maharaj, 2005). Along with the introduction of Artemisinin-based combination therapy (ACT) and rapid diagnostic tests (RDTs) at primary health care facilities in 2000 to streamline diagnosis and improve treatment efficacy, national policy also re- established DDT as a more effective insecticide for IRS. Case numbers have declined substantially over the last decade as a result, and the country has since adopted a strategy for the elimination of malaria by 2018.

Supplementary interventions in South Africa include active case surveillance and health promotion. Insecticide-treated bed nets (ITNs) and larviciding also form part of the national control strategy albeit to a limited extent.

1.8 Regional intervention

Although South Africa had been successful in pushing back the boundary of malaria to its very north-eastern edges over the past five decades, the goal of elimination of malaria from these border areas remained elusive while no effective control programme was in place immediately across the border. It was clear that regional strategies were required to produce the desired impact (Sharp and le Sueur, 1997).

The Lubombo Spatial Development Initiative (LSDI) was launched in 1999 between Mozambique, South Africa and Swaziland through signed agreement by the then presidents of the three countries. The initiative sought to reduce case numbers in the shared border areas of the three countries by extending effective control strategies into Swaziland and Mozambique (Sharp et al, 2007).

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The initiative was credited with massive and sustained reductions experienced largely in the province of KwaZulu Natal, while more moderate reductions were noted in Mpumalanga. However, the lack of effective control in Zimbabwe and in the central provinces of Mozambique translated into fewer gains in the northern reaches of the country, more specifically in Limpopo Province.

Several attempts by the South African National Department of Health over the last six years to attract funding in order to launch similar regional efforts with northern Mozambique, Zimbabwe and Botswana have met with limited success. Most notable amongst these have been the Trans-Limpopo Malaria Control Initiative (TLMI), the Trans-Zambezi Malaria Control Initiative (TZMI) and, more recently, the Trilateral Malaria Control Initiative between Mozambique, Zimbabwe and South Africa (MOZIZA).

1.9 Feasibility of malaria elimination

The feasibility of malaria eradication and elimination is currently under debate globally. While malaria eradication involves the end of malaria transmission globally, elimination refers to a break in transmission either regionally or nationally and control is indicative of a situation where transmission persists although malaria is clinically contained (Greenwood, 2008).

Following the breakdown of the global malaria eradication programme in the late 1960s, the eradication of malaria has largely been abandoned as a feasible target and endemic countries have fallen back to national strategies based on effective control of the disease and its transmission (Greenwood, 2008). Malaria control has received renewed support over the last two decades, enjoying large scale funding from agencies such as the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM) and the President’s Malaria Initiative (PMI). The resuscitation of the eradication agenda in 2007 by the Bill and Melinda Gates Foundation has engendered international commitment to regional and national elimination strategies, seen as a more realistic medium-term goal for endemic countries on the fringe of distribution.

The South African National Malaria Elimination Strategy (2010) notes a reduction in malaria incidence from 1.61 to 0.71 cases per 1000 population at risk between 2000 and 2009, with credit for reductions attributed to a high coverage rate of the national control strategy based largely on IRS together with improved case management, surveillance, health promotion and epidemic preparedness and response (EPR). The broad goals of the national elimination strategy are to achieve malaria elimination by 2018, to achieve malaria elimination certification from the World Health Organisation (WHO) by 2021 and to prevent reintroduction of local transmission thereafter.

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The feasibility of achieving malaria elimination nationally by 2018 is jeopardised in large part by the inability of the country to control malaria effectively along its north eastern borders. Adequate surveillance of malaria cases in the country is critical to the attainment of control and elimination targets, and an understanding of the spatial and temporal distribution of cases at the local level is likely to prove key to the targeting of appropriate interventions and effective strategies to this end.

1.10 Motivation

Border areas of South Africa in which malaria transmission is concentrated have experienced falling case numbers over the last two decades with the expansion of a regional control initiative into Mozambique and Swaziland. However, malaria cases in Limpopo Province persist in relatively high numbers, and distribution patterns appear unchanged over the last decade.

It is proposed that a spatial analysis of malaria case incidence over the last decade and a half will provide invaluable insights into the patterns of distribution of the disease both spatially and temporally. Such evidence may prove key to the appropriate targeting of interventions in the campaign to push back the frontier of malaria and to achieve elimination both in the province and in the country at large.

1.11 Aim and objectives

The aim of this study is to determine whether spatial analysis of malaria distribution both over extended periods of time and during the months of the peak season can provide an understanding of the location of possible malaria spatio-temporal hot spots.

Specific objectives of the study are:

 to describe the dynamics of malaria surveillance in Limpopo Province,

 to analyse the spatial and temporal distribution of malaria in Limpopo Province over 12 malaria seasons dating from 1999/2000 to 2010/2011,

 to utilise spatial statistics to explore spatio-temporal clustering of malaria cases at the locality level over 12 malaria seasons dating from 1999/2000 to 2010/2011

 and to assess the usefulness of spatial statistics in understanding malaria distribution and targeting public health interventions appropriately

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1.12 Study area

Limpopo Province is one of three malarious provinces of South Africa and shares international borders with Mozambique in the east and Zimbabwe in the north.

Located in the northern-most part of the country, the province comprises five districts and twenty six municipalities, with its border with Mozambique in the east buffered substantially by the Kruger National Park (Figure 4).

Figure 4: Malarious provinces of South Africa and endemic districts of Limpopo Province

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The Limpopo Province Malaria Control Programme (MCP) is responsible for the surveillance and control of malaria in the province. The Programme is supported in its work by the National Department of Health’s Malaria Directorate, the Malaria Research Unit (MRU) of the South African Medical Research Council (MRC) and the Vector Control Reference Unit (VCRU) of the National Institute of Communicable Diseases (NICD).

The MRC provides scientific and technical support to the Limpopo MCP and has access to these data for the purposes of research. Use of the data for this study has been approved by the Limpopo Province MCP.

1.13 Approach

Malaria case data for the province is available at the geographical levels of locality, health facility, sector, municipality and district and at the temporal levels of the calendar week, month and year and the malaria season (July of one year to June of the next).

Descriptive summary statistics, conventional chloropleth mapping, directional distribution diagrams based on the standard deviational ellipse and kernel density estimate maps will be used to depict the distribution of malaria across the twelve seasons, from 1999/2000 to 2010/2011.

Spatial high and low cluster analysis using the Getis-Ord General G statistic, spatial autocorrelation using the Global Moran’s I statistic, local high and low cluster analysis using the Anselin Local Moran’s I statistic and hot spot analysis using the Getis-Ord Gi statistic will be conducted to establish the presence of malaria hot and cold spots across the malaria seasons under study.

Finally, provincial IRS activities will be mapped and analysed in relation to the identified malaria hot spots in order to provide insights in the targeting of interventions.

1.14 Tools

Preparation and querying of malaria case data has been conducted using the Microsoft Access (Microsoft, 2010) and Microsoft SQL (Microsoft, 2008) database environments.

Statistical analysis and display of the data has been performed using Microsoft Excel (Microsoft, 2010) and R statistical software (R Development Core Team, 2008).

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Mapping and spatial analysis has been conducted using ArcGIS 10.0 (ESRI, 2010).

1.15 Scope and limitations

In the face of persistent malaria case numbers beyond the expected in view of the comprehensive IRS programme in place, the Limpopo Province MCP needs a fresh perspective on the spatio-temporal patterns of malaria case burdens in the province.

An appraisal of case data for the province at various geographical levels will answer the key questions of distribution and intensity and will provide an appreciation of trends over time and space in order to guide control activities and target field based interventions appropriately.

Issues that will not be discussed here include the impact of climatic factors on malaria distribution and the prediction of risk or distribution for future time periods.

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2 Malaria Surveillance in Limpopo Province

2.1 Monitoring and evaluation

Monitoring and evaluation involves the measurement of the effectiveness of public health interventions, where monitoring describes the ongoing and often routine collection of information regarding the implementation of the intervention and relevant impact indicators, and evaluation refers to the scrutiny and interpretation of such data to establish whether the desired effect of the intervention has been achieved. Interventions such as improved case management and IRS may be measured by assessing the coverage achieved by the intervention, the reduction in morbidity and mortality in the human population and reductions in numbers and infection of the vector population. Monitoring and evaluation is key to the informed management of interventions and their appropriate targeting.

2.2 Surveillance

Surveillance of malaria cases in Limpopo Province provides an understanding of both the intensity and distribution of malaria that is fundamental to the planning, implementation, informed management and evaluation of the field based intervention programme consisting principally of indoor residual spraying (IRS) in communities at risk.

The three provincial malaria control programmes of South Africa operate a computerised Malaria Information System (MIS) that records all cases notified at health facilities as well as those detected during field surveillance (Martin et al, 2002).

The information system also records daily activities of the IRS programme and is able to provide a measure of coverage over both time and space (Booman et al, 2003).

Recently incorporated as a formal aspect of the provincial information system, digital records of entomological surveillance are also routinely maintained.

The MIS is spatially enabled and all malaria cases and IRS activities are recorded at several administratively- and operationally-defined geographical levels.

2.3 Notification

Malaria cases are reported either passively when patients present at a health facility or actively where infections are detected during field surveillance. Cases notified passively at a health facility are entered onto the clinic or hospital case register and are

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further recorded on an official notification form termed the GW17/5 (Appendix A).

Case notification forms are faxed to the district health office, and are then forwarded on to the sub-district (municipal) health office. Forms are delivered or faxed to the provincial Malaria Control Programme where data are entered into the MIS (Figure 5).

Figure 5: Process of passive notification of malaria cases in South Africa

Case investigation forms are generated from the system and issued to case surveillance officers or case investigators to facilitate follow-up and investigation.

Between five and six case investigators are assigned to a district and each investigator is allocated a set of health facilities to monitor.

Passively notified cases are investigated thoroughly upon notification to verify the diagnosis and to determine the possible source of the particular infection. Further information about the case elicited during follow-up are entered into the system upon receipt of the case investigation form termed the EP6 (Appendix B) and are linked to the original case record via a unique patient record identifier (Figure 6).

Although prompt investigation within two to three days of notification is desirable, delays may occur at the health facility level during shift changes or as the result of staff turnover. In addition, most primary health facilities are not equipped with telephones, fax machines or computers and notification forms must often be collected physically by malaria case investigators during regular facility visits.

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Figure 6: Process of investigation of passively notified malaria cases in South Africa

Figure 7: Process of IRS and related reporting in South Africa

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2.4 Intervention

A Spray information System (SIS) was implemented in Limpopo Province in 2006 and hard copy records of IRS activity in the field are now entered into the computerised system. Individual spray officers fill out a daily activity record card known as an SP1 (Appendix J). Spray team leaders summarise the daily activity records of individual officers onto a team record form termed the SP2 (Appendix K). These team summary cards are then submitted to the MCP on a daily basis where the data are entered into the SIS.

2.5 Data

The malaria case database records patient particulars (name, age and gender), residential details of the patient (province, district, locality and village of residence), where the case was contracted (probable source locality, district, province or country of infection), the health facility at which the case was notified (facility name, locality and district), rapid diagnostic test results, lab analysis results if blood samples were taken, treatments administered, if any and outcomes of the case including referrals, if these occurred.

Data collected during case follow-up and investigation includes recent travel history and details of previous episodes of malaria.

Data fields captured within the IRS database include details of the spray officer and the team, the locality in which activity was conducted, the type and quantity of insecticide used, the type of spray nozzle used on the pump, the number of structures sprayed, the number not sprayed and possible reasons for not spraying.

Unsprayed structures are revisited at the end of the spray round and spraying conducted at this time is referred to as a mop-up, with each spray record in the database classified according to this distinction of a main spray activity or a mop-up.

Spray activity is further recorded at the levels of locality and malaria planning area, termed a sector.

2.6 Reporting and response

Case reports drawn from the MIS to inform management and provide decision support include daily, weekly and monthly summaries by province, district, municipality and sector.

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A threshold monitoring system is also employed to determine where outbreaks occur and to facilitate appropriate response from the control programme, with an alert and an action threshold calculated at the health facility and the municipality levels, and against which case numbers are examined on a weekly basis to determine whether additional intervention is required (Coleman et al, 2008).

Weekly and monthly reports generated by the SIS enable monitoring of progress and coverage of IRS activities and inform planning and management of the intervention.

2.7 Evolving geographies

The administrative geography of South Africa of the 1990s was based on the major delineations of province and magisterial district, with different sectors of administration using separate boundary definitions for management and reporting. At the time, the country was divided into 9 provinces, 74 magisterial districts and 53 health districts, and all malaria reports were issued at these official levels.

With the collapse of apartheid and the advent of democracy in 1994, the administrative division of the country was reviewed and amended to provide more equitable distribution of services and resources to previously disenfranchised population sectors.

Following a period of transitional geography, a new system of administration was finally introduced in 2001 that replaced the old magisterial districts and also removed the sector-specific planning districts in an effort to ensure streamlined and consistent planning, service delivery and reporting across major organs of state. The new geography divided the country into 9 provinces, 52 districts and 274 municipalities.

Although a relatively stable system since its introduction, these official delineations are in a general state of flux as political decisions frequently involve the reassessment of boundary lines.

The version of boundary delineations utilised for this study are based on the official release of 2008 from the Municipal Demarcation Board of South Africa, the authority appointed by the state for the development and management of these data.

Cross-border municipalities delineated in 2001 and falling in part into both Limpopo and Mpumalanga Provinces included the Greater Marble Hall, Greater Tubatse and Greater Groblersdal (subsequently renamed Elias Motsoaledi) municipalities. These municipalities have since been allocated in their entirety to Limpopo Province and are included in this analysis throughout the reporting period. Further, Bushbuckridge Municipality was reallocated from Limpopo Province to Mpumalanga in 2005 and has been excluded from all analysis.

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2.8 Data quality

Insufficient quality checks coupled with a massive quantity of data collected within the MIS nationally over the last two decades has resulted in divergent and inconsistent geographies being recorded in the systems that defy accurate mapping or spatial analysis. A large scale data cleaning exercise conducted by the Health GIS Centre of the Malaria Research Unit of the Medical Research Council of South Africa over the last seven years has now rendered this data clean and suitable for analysis.

Adjustments to the provincial malaria case data in Limpopo Province have included the comprehensive review, update and correction of the allocation of malaria cases from the old administrative geography of the country to the new delineations adopted in 2001. To achieve this, all localities where a malaria case was notified over the entire reporting period since the inception of the digital MIS in 1997 were georeferenced using existing official spatial reference data sets for the country, with each assigned a latitude and longitude. The resulting point data set was then overlaid on the official administrative boundaries and the correct administrative hierarchy of names and codes for districts and municipalities assigned using the geographical location to join the data layers.

Further data quality issues addressed during the process of data cleaning included the removal of duplicate locality entries, the correction of spelling errors, the standardisation of locality name prefixes attributable to local language influences, the identification and normalisation of alternate locality and community names and the removal of all special characters from the name set. The exercise of geographical data rectification and standardisation involved the labour of some five technicians in total working together in various combinations over a seven year period dating from 2005 to 2011.

The Limpopo Province MCP is able to produce routine reports of case totals at various administrative levels and at the level of individual health facilities, and to indicate where weekly thresholds have been exceeded at each of these levels. However, the programme faces considerable challenges in reporting conclusively with respect to sources of infections in the province. While data cleaning has rendered the data more accurate, the completeness of the data is continually impacted upon by the quality of data collection in the field.

2.9 Spatial data and mapping

Although all spatial entities in the MIS have been corrected and are available to support the mapping and spatial analysis of the case data recorded between 1997 and

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the present date, the skills and capacity to engage with the data at this level are not represented at the provincial level.

Although effort has been made to appoint and capacitate MCP information officers to manage data flow, quality and reporting, staff retention has proved an issue and high turnover of personnel has prevailed, with large periods observed with no appropriate personnel in place. As a result, all spatial mapping and analysis support to date has been largely provided by the Health GIS Centre of the Medical Research Council of South Africa.

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3 Distribution of Malaria in Limpopo Province

3.1 Introduction

The population of Limpopo Province was reported at 5 554 657 in 2011, with a mean growth rate of 1.13% observed annually between 2000 and 2011 (Statistics South Africa, 2011). The Limpopo Province Malaria Control Programme (MCP) identifies Vhembe and Mopani Districts of the province as areas of high malaria risk, with 46.6%

of the its population resident there. The province covers an area of 126 145.6 km², with Vhembe and Mopani accounting for 28.3% of the land space and representing an area of dense population settlement. The districts of Waterberg, Capricorn and Greater Sekhukhune consist of agricultural and farm land in large part. A considerable proportion of the country’s mining activities are also located in Limpopo Province. The Musina-Beitbridge border post located in the north of the province serves as the point of entry for large numbers of immigrants originating predominantly from Zimbabwe and from elsewhere across the central and southern African regions.

Blanket surveillance and control activities are conducted in Vhembe and Mopani, with targeted intervention applied in isolated areas of the Waterberg, Capricorn and Greater Sekhukhune Districts where malaria cases are reported regularly. Although some of the cases reported in these low risk areas are reported to be locally contracted, the correct classification of these cases is under dispute and the accurate identification of transmission zones in the province remains elusive as a result.

A paucity of literature has been noted over the last decade with respect to the national epidemiology of malaria in South Africa, as indeed from a provincial perspective. In 2007, the National Department of Health’s annual report on the prevalence and distribution of malaria in South Africa reported a total of 2 898 cases and 34 deaths in Limpopo Province, down from 6 144 cases and 53 deaths in 2006, albeit with the net effect of an increase in the case fatality rate from 0.8 to 1.1 between the two years.

The province was further reported to contribute a substantial 44% to the country’s case burden during the 2006/2007 malaria season. For the same period, Gerritsen et al (2008) reported slightly different figures of 2 738 malaria cases and 33 deaths in the 2006/2007 malaria season, down from 6 229 cases and 52 deaths during 2005/2006.

The National Department of Health’s statistical release (reproduced on HST web portal, 2011) reports 2 398 cases and 21 deaths in Limpopo Province in 2011, down from 4 174 cases and 35 deaths in 2010.

The various districts of the province fall into different categories of the continuum of malaria elimination (Mendis et al, 2009). According to the National Malaria Elimination Strategic Plan (2011), Vhembe District is in the control phase with a case incidence of

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0.33 to 12.74 per 1 000 persons at risk, Mopani in pre-elimination with an incidence range of 0.15 to 13.09, and Capricorn, Greater Sekhukhune and Waterberg in elimination with a range of incidences of under 0.12, under 0.06 and under 1.00 respectively.

With the country actively embracing its national strategy for elimination by 2018, the reality of malaria in Limpopo Province appears far removed from this goal. Persistently high case numbers coupled with increasing levels of imported malaria and no conclusive evidence of the extent of local transmission will work to keep the focus in Limpopo Province firmly on control, at the very least for the next five years.

3.2 Data

Malaria cases are recorded in the Limpopo MIS by the place of residence of the patient, the health facility at which the case was notified and the place of possible source of the infection. The date of notification of the case and the date on which the blood smear was taken are recorded in the system.

Case data for this analysis has been extracted from the MIS at the levels of locality, municipality and district of the patient’s place of residence. Population data to support incidence calculations has been extracted from the 2001 National Population Census and the 2007 National Community Survey (Statistics South Africa).

3.3 Limitations

Analysis is based on all reported malaria cases for the seasons under study. The completeness of case records is impacted upon by a number of factors. These may include administrative errors that result in certain health facilities not reporting into the system at all or case notification forms from known facilities not being submitted to the Malaria Control Programme (Cibulskis et al, 2011).

Completeness of case data that has been entered into the system may be affected where the notification form has not been filled out accurately or where important pieces of information have been omitted. In the case of private practitioners, information regarding the possible source of the infection is most often excluded and attempts on the part of the MCP to obtain such information after the patient has presented proves challenging. These various factors may result in an under-reporting or an over-reporting of cases in a particular place or at a particular time.

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An area of concern in terms of possible missing data is the municipality of Maruleng which borders Greater Giyani Municipality in the north, Kruger National Park in the east and Mpumalanga Province in the south. Maruleng shows extremely low cases in some seasons and no cases at all in others, in stark contrast with case burdens reported by its immediate neighbours. It may be theorised that case records have gone astray or are being mismanaged administratively and do in fact exist though they may not be reported via official channels.

Enquiries made with the MCP have elicited no conclusive information with respect to possible missing data and further investigation is required. The possibility of missing data for this municipality may have considerable impact upon the accuracy of spatial patterns observed in the south eastern part of the province as a result.

3.4 Methods and results

For the purposes of this analysis, and due primarily to incomplete records of the source of infections, case data was extracted at the geographical levels of locality, municipality and district of the place of residence of the patient. As a result, local cases have not been differentiated from imported ones, and all cases notified within the province have been treated equally. The malaria season was selected as the appropriate temporal scale of all analysis, and was based on the blood smear date.

3.4.1 Distribution

Malaria cases and deaths recorded across the entire province were summarised for each malaria season for the period 1999/2000 to 2010/2011 (Table 1). Mean cases per municipality per season were calculated taking into account all reporting months and including months with no cases reported.

Case fatality rates were calculated per season as the number of deaths per the cases reported for that period. The mean monthly incidence per 100 000 population at risk and the corresponding 95% confidence interval were calculated.

In order to calculate malaria case incidence per population at risk, population data was extracted from the 2001 census and the 2007 community survey at the level of municipality. Annual incremental growth rates observed between the two census years was calculated and then adjusted using the annual provincial growth rate reported during the mid-year population survey of each year. The adjusted rates were used to calculate population at municipality level for non-census years between 2001 and 2007 and to project population forward from 2008 to 2011. Incidence was then

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calculated as the number of cases per 100 000 persons at risk or per 100 000 person years.

Substantial reductions in total malaria cases are observed in the province over the last twelve years, from 6 895 cases in 1999/2000 to 3008 cases in 2008/2009 (Table 1).

However, some increase has since been noted over the last two seasons with 3277 cases recorded in 2009/2010 and 4 793 cases in 2010/2011. A mean case count of 184.35 per municipality was observed in 2010/2011. That season also recorded 53 deaths and a case fatality rate of 1.11 for the province, up from under 1 over the previous three seasons. A mean incidence of 1 119.98 malaria cases per 100 000 person years was observed in 2010/2011, up from 76.23 in 2008/2009 and 83.17 in 2009/2010.

Malaria season

Total malaria

cases

Mean cases per municipality

Total deaths

CFR (%)

Total population

at risk

Mean incidence per municipality per 100,000

person years (95% CI) 1999/2000 6,895 287.29 68 0.99 4,797,708 193.09 (19.43 - 366.76) 2000/2001 8,763 365.13 72 0.82 4,895,620 211.94 (23.22 - 400.67) 2001/2002 5,561 231.71 46 0.83 4,995,531 169.67 (8.08 - 331.26) 2002/2003 4,710 214.09 56 1.19 5,036,385 127.96 (4.38 - 251.55) 2003/2004 5,427 246.68 77 1.42 5,073,587 149.47 (18.94 - 280) 2004/2005 4,160 189.09 45 1.08 5,106,807 121.82 (0.24 - 243.4) 2005/2006 5,599 233.29 46 0.82 5,137,312 149.33 (9.8 - 288.86) 2006/2007 2,757 114.88 33 1.20 5,164,565 79.73 (3.89 - 155.57) 2007/2008 4,803 208.83 35 0.73 5,238,298 123.64 (-0.61 - 247.88) 2008/2009 3,008 125.33 25 0.83 5,269,570 76.23 (6.65 - 145.8) 2009/2010 3,277 126.04 32 0.98 5,296,033 83.17 (12.45 - 153.89) 2010/2011 4,793 184.35 53 1.11 5,322,629 119.98 (17.89 - 222.07) Table 1: Malaria incidence and deaths by season in Limpopo Province (1999/2000 to

2010/2011 malaria seasons)

The number of cases and deaths were compared by season (Figure 8). The general trend of reductions between 1999/2000 and 2004/2005 was followed by some increase over subsequent seasons albeit with considerable fluctuation evident. Case numbers and deaths appear on the incline over the last two seasons. The box plot of malaria cases by season for the reporting period 1999/2000 to 2010/2011 illustrates the rise in mean cases per municipality over the last three malaria seasons (Figure 9).

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Figure 8: Cases and deaths by malaria season in Limpopo Province (1999/2000 to 2010/2011)

Figure 9: Box plot of mean malaria cases per municipality per malaria season Total cases per month per season were extracted in order to establish monthly trends and seasonality of malaria in Limpopo province (Table 2). June, July and August were observed to be low case months, with cases beginning to increase in September, peaking first in November or December and again in April before declining in May. The first peak corresponds with the start of the rainy season and both peaks coincide with periods of increased travel associated with the Christmas and Easter holidays.

1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 2010/2011

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Trends in reported malaria cases at district level over the malaria seasons from 1999/2000 to 2010/2011 were examined (Figure 10). Mopani and Vhembe Districts were observed to record the highest numbers of cases, reporting 1269 and 3117 cases respectively during the 2010/2011 season, with the other three districts reporting 407 cases collectively for the same period. The trend at district level mirrors the provincial picture, with increases noted in all districts in the last season. Despite a minor decline in cases between 2008/2009 and 2009/2010, Vhembe District shows marked increase in 2010/2011, growing by 63% from 1987 cases to 3117 over a single season.

Malaria Season Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun ∑ 1999/2000 60 154 569 586 1331 946 650 425 919 859 288 108 6895 2000/2001 89 108 167 1614 2086 1098 950 744 422 530 842 113 8763 2001/2002 47 35 519 625 814 571 755 651 705 509 256 74 5561 2002/2003 36 39 409 255 243 444 1648 559 261 427 330 59 4710 2003/2004 43 33 245 1094 538 857 220 170 961 606 537 123 5427 2004/2005 49 58 566 499 530 331 1289 283 234 128 149 44 4160 2005/2006 35 68 192 165 60 356 1643 1682 822 338 203 35 5599 2006/2007 23 95 185 313 285 243 215 222 148 686 315 27 2757 2007/2008 33 20 312 368 189 350 453 455 1527 648 360 88 4803 2008/2009 46 26 269 191 182 152 331 412 923 205 186 85 3008 2009/2010 42 18 132 276 154 389 677 381 312 650 182 64 3277 2010/2011 33 11 162 722 217 803 871 338 482 874 198 82 4793

Table 2: Malaria cases per month per season in Limpopo Province (1999/2000 to 2010/2011 malaria seasons)

Mean cases, deaths, case fatality rates, mean local cases, proportion of local cases and mean incidence per 100 000 person years were also calculated per district for the entire reporting period from the 1999/2000 to the 2010/2011 malaria season (Table 3). Mean incidences per season per 100 000 person years of 138.77 and 263.56 were observed for Mopani and Vhembe Districts compared with lower incidences of 5.39, 8.51 and 24.97 for Greater Sekhukhune, Capricorn and Waterberg Districts respectively. Mean deaths per season of 29.50 in Vhembe and 14.33 in Mopani were noted. The mean proportion of local cases per season stood at around 58% for Mopani and 70% for Vhembe.

The incidence of malaria cases per 100 000 person years was calculated per municipality per malaria season for the reporting period ranging from the 1999/2000 to 2010/2011 malaria seasons (Table 4). Municipalities consistently reporting an incidence in excess of 50 cases per 100 000 person years over that period included

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Mutale, Musina, Greater Giyani, Ba-Phalaborwa, Thulamela, Lephalale, Makhado and Greater Tzaneen. Of these, Mutale and Musina accounted for by far the highest incidences in all years, with 817.32 in Musina and 987.66 in Mutale in the 2010/2011 season. These were followed in that season by Ba-Phalaborwa with an incidence of 242.74 and Greater Giyani with 255.91 per 100 000 person years. Increases in incidence of 39% were noted in both Musina and Mutale Municipalities between 2009/2010 and 2010/2011.

Figure 10: Malaria cases per district per season, Limpopo Province (1999/2000 to 2010/2011 malaria seasons)

District

Mean cases per season

Mean deaths

per season

Mean CFR

(%) per season

Mean local cases per season

Mean proportion local cases per season

Mean incidence per 100,000 person years

(95% CI) Capricorn 100.25 3.20 3.05 52 45.34 8.51 (5.64 - 11.39) Greater Sekhukhune 54.50 2.20 1.78 25 47.11 5.39 (4.07 - 6.72) Mopani 1478.58 14.33 1.01 828 58.33 138.77 (105.74 - 171.81) Vhembe 3193.00 29.50 0.91 2279 70.11 263.56 (210.41 - 316.71) Waterberg 153.08 2.38 1.01 38 23.53 24.97 (20.18 - 29.76)

Table 3: Malaria incidence and deaths by districts of Limpopo Province (1999/2000 to 2010/2011 malaria seasons)

Spatial Analysis and Mapping of Malaria in Limpopo Province, South Africa