Household Mapping App Download
Gary Tiboni
Background: Household overcrowding is a serious public health threat associated with high morbidity and mortality. Rapid population growth and urbanisation contribute to overcrowding and poor sanitation in low-income and middle- income countries, and are risk factors for the spread of infectious diseases, including COVID-19, and antimicrobial resistance. Many countries do not have adequate surveillance capacity to monitor household overcrowding. Geostatistical models are therefore useful tools for estimating household overcrowding. In this study, we aimed to estimate household overcrowding in Africa between 2000 and 2018 by combining available household survey data, population censuses, and other country-specific household surveys within a geostatistical framework.
Methods: We used data from household surveys and population censuses to generate a Bayesian geostatistical model of household overcrowding in Africa for the 19-year period between 2000 and 2018. Additional sociodemographic and health-related covariates informed the model, which covered 54 African countries.
Findings: We analysed 287 surveys and population censuses, covering 78 695 991 households. Spatial and temporal variability arose in household overcrowding estimates over time. In 2018, the highest overcrowding estimates were observed in the Horn of Africa region (median proportion 62% [IQR 57-63]); the lowest regional median proportion was estimated for the north of Africa region (16% [14-19]). Overall, 4744 million (95% uncertainty interval [UI] 2501 million-7407 million) people were estimated to be living in overcrowded conditions in Africa in 2018, a 627% increase from the estimated 2915 million (1808 million-4173 million) people who lived in overcrowded conditions in the year 2000. 485% (2299 million) of people living in overcrowded conditions came from six African countries (Nigeria, Ethiopia, Democratic Republic of the Congo, Sudan, Uganda, and Kenya), with a combined population of 5383 million people.
Housing mapping and household enumeration are essential for the planning, implementation, targeting, and monitoring of malaria control interventions. In many malaria endemic countries, control efforts are hindered by incomplete or non-existent housing cartography and household enumeration. This paper describes the development of a comprehensive mapping and enumeration system to support the Bioko Island Malaria Control Project (BIMCP).
The housing unit mapping and household enumeration system developed for Bioko Island enabled the BIMCP to more effectively plan, implement, target, and monitor malaria control interventions. Since 2014, the BIMCP has used the unique household identifiers to monitor all household-level interventions, including indoor residual spraying, long-lasting insecticide-treated nets distribution, and annual malaria indicator surveys. The coding system used to create the unique housing unit and household identifiers is highly intuitive and allows quick location of any house within the grid without a GPS. Its flexibility has permitted the BIMCP to easily take into account the rapid and substantial changes in housing infrastructure. Importantly, by utilizing this coding system, an unprecedented quantity and diversity of detailed, geo-referenced demographic and health data have been assembled that have proved highly relevant for informing decision-making both for malaria control and potentially for the wider public health agenda on Bioko Island.
Effective resource allocation for malaria control depends on accurate denominators that define and fully characterize the relevant intervention universe [13]. Among the challenges faced by the BIMCP during the initial phase of the project was the limitation of operating without detailed housing unit cartography and household enumeration to support effective planning and implementation of interventions. Households were counted within communities of which there was limited spatial knowledge. The absence of a geo-referenced households database thus translated into uncertain and inaccurate knowledge of the true denominator of units for intervention and, therefore, into sub-optimal planning, targeting and coverage monitoring [1]. The impact of these limitations was further exacerbated by the rapid growth in housing stock associated with the booming oil-driven economy, including substantial growth in informal urban housing in Malabo.
Accurate household enumeration, therefore, is key for the implementation of household-based interventions. Moreover, there is an increasing need for scaling-up or adapting malaria control and much of this can be informed by wide-scale household surveys [14]. Notwithstanding, for many malaria control programmes, enumeration methods remain rudimentary or nonexistent and relatively little effort seems to have been invested in instituting mapping and enumeration systems as part of malaria control programmes. Recently, new methodologies have been tested driven by the need to reassess vector control strategies towards more targeted approaches in sub-Saharan malaria endemic areas [15, 16]. In Mozambique, houses in Mopeia district were enumerated by the use of GPS and satellite imagery triangulation; satellite images allowed the identification of areas that had been missed in the first round of enumeration that later were revisited for inclusion. In Zambia, open-source satellite imagery was used for household enumeration in 15 districts and proposed as a more efficient alternative to field enumeration methods. This methodology was tested in rural areas where housing density was low and atmospheric conditions proved favourable (i.e. limited cloud cover), and was used later for targeting IRS activities [17].
This paper describes the experience developing a cartographic system to map housing units and enumerate households on Bioko Island and how it has enabled the BIMCP to substantially improve its planning, service targeting, impact and coverage monitoring, to enhance the effectiveness of its interventions and to ultimately achieve a continued reduction of malaria burden on the island. The system was developed with the approval and collaboration of the NMCP. For clarity, the paper refers to buildings destined for human habitation as houses or housing units, and as households to groups of people that reside in a housing unit and who regularly share meals together. Therefore, housing units may be occupied (have a resident household) or unoccupied (have no resident household).
The process of mapping housing units and enumerating households was initiated on a pilot basis in one community in 2011, extended to 18 sentinel sites [2] later in the same year, and systematically implemented on the whole island by 2014. Bespoke application software was used to support enumeration and data entry in the field, at first with the use of electronic personal digital assistants (PDAs) and later through the use of tablet computers. The methodology involved developing a consistent coding system for uniquely identifying and mapping housing units with the aid of remote sensing imagery and global positioning systems (GPS), as well as geo-referencing them through an interaction between teams collecting data in the field (i.e. ground truth of locations) and data analysts assembling the information on a geographical information system (GIS). The ultimate aim of this process was to have a complete geographical database of housing units, each assigned a unique identifier and linked to several attributes, including housing occupancy and household information. This methodology is described in more detail below.
High-resolution satellite imagery was available between 2011 and 2014 only for the map-sectors in the Malabo area and surroundings [18]. In these cases, mapping teams were provided with printed maps containing the satellite image and the limits of the map-sector. In map-sectors for which satellite imagery was unavailable, or where such imagery was of a poor resolution or was cloud-obscured, the teams used the map-sector limits overlaid on detailed hand-drawn maps of streets, roads and pathways produced by the BIMCP using GPS, where they drew in buildings, locating these in space relative to these features. This was the case for most of the areas outside of Malabo, including rural and peri-urban areas. However, as updated, extended and improved resolution satellite imagery became progressively available over time, map-sector maps were converted from the GPS-coordinate dependent hand-drawn maps to satellite-based maps.
By the close of the first phase of the mapping of housing units and enumeration of associated households in 2014, 78,524 housing units had been mapped. Due to the rapid increase in housing infrastructure over time, by mid-2018, 18,524 new housing units had been added to the database to total 97,048 houses within 251 map-areas and 4467 map-sectors (Fig. 1), of which 68,619 (70.7%) were occupied. This represented an overall increase of 23.6% in the housing stock since the baseline mapping was completed, or an average 5.9% increase per annum (Table 1). Figure 3 illustrates mapped housing units in a high population density urban setting and in a low population density rural setting together with their unique identifiers. In these examples, there were 4373 mapped housing units in urban map-area M0277 compared to 164 in rural M0504, and 73 and 11 mapped houses in map-sectors M0277S080 and M0504S017, respectively. Housing density by map-area was highly skewed, ranging from 1 to 6840 housing units, with a median of 52 (Fig. 4). Fifty six percent of mapped housing units were concentrated in only 13 (5.2%) map-areas. The city of Malabo accounted for 83.8% of all houses on the island, with a median number of housing units per map-area of 707. House density at the map-sector level ranged between 1 and 227, with a median of 10. Over one quarter (26%) of mapped housing units were contained within 5.1% of map-sectors on Bioko.
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