Is there a relationship between poverty and lack of access to
the TB DOTS programme in urban Malawi?. Kemp J 1,2, Boxshall M 3,
Nhlema B 1, Salaniponi FML 4, Squire SB 1,2 _________Equi-TB
Knowledge Programme The Malawi Equi-TB Knowledge Programme is a
collaboration between: Liverpool School of Tropical Medicine
National TB Programme, Malawi Department of Sociology, University
of Malawi Funded by the Department for International Development
(DFID), UK Addresses: 1 Equi-TB Knowledge Programme, Lilongwe,
Malawi 2 Liverpool School of Tropical Medicine, Liverpool, UK. 3
GIS Consultant, Lilongwe, Malawi 4 National TB Control Programme,
Lilongwe, Malawi. Methods An electronic base map of urban Lilongwe
was provided by Lilongwe City Assembly. Health facility locations
(public and private) were mapped using a Global Positioning System
receiver and projected onto base map. Selected data from the 1998
national census were incorporated and linked to the map data by
City Area. Year 2000 chronic cough register data for all public and
larger private health health facilities were entered into Epi Info
v6.04. Variables include Area of residence, age, sex and sputum
smear result of TB suspects. Chronic cough register data were
linked to the base map, again using City Area as the common field.
Maps of broad indicators of poverty were compared to maps of
incidence of chronic cough and smear positive TB Area 56 Mtsilisa
and Ntandile High density, unplanned squatter settlement Area 3 Low
density, planned settlement Area 18 High density, planned
settlement Area 47 Medium density, planned settlement Aim To
explore the geographical relationship between poverty and
tuberculosis (TB) in urban Lilongwe, Malawi Objectives To develop a
geographical information system (GIS) for the spatial analysis of
poverty To map prevalence of chronic cough and smear positive TB
cases in urban Lilongwe To analyse the spatial relationship between
poverty and registered TB cases Background Malawi has a wealth of
spatially referenced data suitable for GIS mapping, including the
results of the 1998 National Census. Lilongwe is a planned city of
numbered City Areas, within each of which housing type and social
amenities are relatively homogenous. This makes it ideal for the
geographical analysis of broad indicators of poverty. Routine
health data rarely provide an indication of socio-economic status
of patients. However, information on area of residence of TB
patients is routinely collected for tracing of defaulters. Using
area of residence as a proxy indicator of socio-economic status
allows an exploration of equity in access to TB care. Figure 1: A
map of urban Lilongwe showing population density (population per
square km) and the location of all public and major private health
facilities. Figure 2: Secondary education Figure 3: Incidence of
chronic cough cases Figure 4: TB cases per 100 chronic cough cases
Figure 2 shows a selected indicator of poverty for numbered City
Areas in urban Lilongwe. Secondary education is a reasonably
sensitive indicator of poverty in urban Malawi (Malawi Integrated
Household Survey,1998). The map shows that high density, unplanned
or squatter settlements (Areas 56, 57 and 24) have lower levels of
secondary education compared to high density planned areas (Area
18), medium or low density planned areas (Areas 47, 9, 3). These
patterns of poverty indicators are consistent with participatory
rankings of Areas of Lilongwe which were carried out with key
informants from the Lilongwe City Assembly and the Ministry of
Health and Population. Figure 3 shows the incidence of chronic
cough cases (per 100,000 population) in the year 2000 for each Area
of Lilongwe. These figures reflect numbers of TB suspects who were
registered at the public or larger private health health facilities
within the city; they are therefore a measure of utlisation health
facilities. The map shows that low density (higher socio-economic
status) Areas have fewer chronic cough cases than medium or high
density, planned Areas (Areas 3 and 9 versus areas 47 and 18).
Counter intuitively, the high density, unplanned Areas (Areas 56,
57 and 24), the poorest Areas in the city, also have fewer chronic
cough cases than medium or high density planned Areas. This may
reflect fewer actual cases or under utilisation of public and major
private health facilities. Figure 4 shows the number of smear
positive TB cases per 100 chronic cough cases. In this map, the
medium and high density planned areas which have the highest
incidence of chronic cough cases have relatively fewer smear
positive TB cases per chronic cough case. In contrast to figure 3,
the high density, unplanned areas have higher numbers of smear
positive TB per chronic cough case. Summary Public health services,
and the TB DOTS programme, are free at the point of delivery and
are geographically accessible to the population of Lilongwe (within
6km). The GIS provides a spatial analysis of poverty for
sub-districts, or Areas, in Lilongwe. Poor areas are associated
with relatively fewer numbers of chronic cough cases, probably
reflecting under-utilisation of health facilities. Poor areas are
associated with a high rate of smear positive TB per chronic cough
case. This is likely to reflect a higher burden of illness or later
presentation at health facilities with symptoms of TB. Using this
geographical analysis, it is estimated that 49% of smear positive
TB cases may be missing from the TB DOTS programme in the poorest
areas of Lilongwe. Area 56 Area 3 Area 18 FINDINGS Table 1:
Estimate of missing TB cases: a comparison between Area 18 (high
density, planned settlement) with Area 56 (high density, unplanned
settlement) Area 47 Table 1 (right) provides a comparison of the
actual numbers and rates per 100,000 of registered chronic cough
and TB cases in Area 18 and 56. These areas have similar population
densities and are adjacent to each other. Making the assumption
that we should expect similar rates of chronic cough and smear
positive TB cases in both areas it is estimated that 1565/100,000
chronic cough and 187/100,000 smear positive TB cases are
unaccounted for, or missing, from Area 18. In actual numbers, this
translates to 350 people with chronic cough, and 42 people with
with smear positive TB, who have not accessed health services (or
the TB DOTS programme) from this Area.