Assessing health impacts in complex eco-epidemiological settings in the humid tropics: Modular baseline health surveys

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Abstract

The quantitative assessment of health impacts has been identified as a crucial feature for realising the full potential of health impact assessment (HIA). In settings where demographic and health data are notoriously scarce, but there is a broad range of ascertainable ecological, environmental, epidemiological and socioeconomic information, a diverse toolkit of data collection strategies becomes relevant for the mainly small-area impacts of interest. We present a modular, cross-sectional baseline health survey study design, which has been developed for HIA of industrial development projects in the humid tropics. The modular nature of our toolkit allows our methodology to be readily adapted to the prevailing eco-epidemiological characteristics of a given project setting. Central to our design is a broad set of key performance indicators, covering a multiplicity of health outcomes and determinants at different levels and scales. We present experience and key findings from our modular baseline health survey methodology employed in 14 selected sentinel sites within an iron ore mining project in the Republic of Guinea. We argue that our methodology is a generic example of rapid evidence assembly in difficult-to-reach localities, where improvement of the predictive validity of the assessment and establishment of a benchmark for longitudinal monitoring of project impacts and mitigation efforts is needed.

Introduction

Health impact assessment (HIA) entails the systematic analysis of potential impacts on public health due to policies, programmes and projects, and aims to optimise the health interests in the decision-making process (Kemm et al., 2004). HIA usually embraces an interdisciplinary approach, combining quantitative and qualitative methods, to guide evidence-based mitigation measures (Krieger et al., 2003, Lock, 2000, Scott-Samuel, 1998). HIA has progressively developed over the past 20 years with continued diversification in approaches, methods, tools and guiding frameworks (Harris-Roxas and Harris, 2011, Krieger et al., 2010). The salient issues in natural resources and industry development projects in the developing world are quite different from those associated with an advanced economy policy or programme. Given the enormous, resource-driven (i.e. biofuels, mining, oil/gas, water and timber) development that is occurring in low-income, but resource-rich countries (Erlanger et al., 2008a), there is a need to identify the most useful approaches and techniques for characterising the baseline situation. Defining the baseline is a crucial exercise, as subsequent monitoring and evaluation (M&E) activities and documentation of positive and negative effects will be dependent on the accuracy of the baseline determination. In a developing country setting, obtaining relevant baseline data in an efficient and cost-effective manner is a complex, yet important undertaking.

In general, HIA practitioners draw on epidemiological evidence that is readily available, and critically assess its relevance for particular circumstances of a specific proposal (Mindell et al., 2004). In a developing country context, population-based surveys such as demographic health surveys (DHS), multiple indicator cluster surveys (MICS) and health statistics reported by the World Health Organization (WHO) and other organisations typically provide epidemiological data on a national or regional level. While such data are relevant for impact assessment of national policies and programmes, they are often inapplicable for M&E of a specific project at a community level. Settings that are characterised by profound micro-environmental differences (e.g. altitude, humidity, land-use patterns, rainfall and temperature), and large disparities of access to health care, have important ramifications on local burdens of disease (Eisenberg et al., 2007, Listorti and Doumani, 2001, Prüss-Üstün et al., 2008, Schellenberg et al., 2003, Utzinger and Keiser, 2006). Regional or national data typically obscure or overtly miss critical small area morbidity/mortality differences. Hence, for robust risk appraisal and documenting changing patterns of health, wellbeing and equity following project implementation, adequate tools for quantification at a local level are required (Bhatia and Seto, 2011, Utzinger et al., 2005, Winkler et al., 2010).

The baseline analysis is tied to, and sequentially follows, the initial scoping analysis. Scoping identifies the range of potential health impacts and determines, by means of a gap analysis, whether sufficient data are available in order to proceed directly with the risk/impact analysis and mitigation phase (Winkler et al., 2011). In case of inadequate or insufficient data, there is a need to collect additional baseline health data. In low-income countries, critical data gaps are the norm rather than the exception (Adrien et al., 2008, Thamlikitkul, 2006). Hence, it is essential to develop a standardised, rapid and inexpensive baseline health survey methodology that incorporates a broad set of practical and readily reproducible key performance indicators (KPIs) that can be adapted to the magnitude and complexity of myriad project settings. In this context, we have developed a modular, cross-sectional baseline health survey methodology that has been successfully applied in a number of projects, countries and environmental settings across sub-Saharan Africa and elsewhere. In the present paper, our methodology is illustrated by a baseline health survey carried out in 14 sentinel sites located within the concession area of a mining project in West Africa.

Section snippets

Key performance indicators (KPIs)

KPIs are measures of project inputs, outputs, outcomes and impacts that are monitored during project implementation (Mosse and Sontheimer, 1996). From a practical point of view, three data collection levels exist, each of which offers a set of specific indicator groups: (i) individual level (e.g. age and sex, indicators of knowledge, attitude and practice (KAP) and biomedical indicators); (ii) household level (e.g. structural indicators, such as durable housing characteristics, asset indicators

Study area and compliance

Our case study pertains to a baseline health survey carried out for the Rio Tinto Simandou project in May 2010. This project is a large iron ore mining exploration currently at feasibility stage, located in the south-eastern part of the Republic of Guinea (Rio Tinto, 2010). An estimated 60,000 people reside in the administrative area around the mine concession, affecting four sub-districts with 31 settlements (ranging from small hamlets with less than 40 individuals to a town with 22,000

Results

To illustrate the methodology, we present selected KPIs pertaining to malaria, helminth infections, sanitation and drinking water, and access to health care. Of note, due to unforeseen circumstances, one of the sentinel sites (i.e. Lamandou) could only be sampled by the parasitological school survey team. Hence, complete data sets are available for 13 of the 14 selected sentinel sites.

Discussion

Quantitative assessments of health impacts and the need for adequate tools and methods have been identified as important features for realising the full potential of a HIA (Bhatia and Seto, 2011, Mindell et al., 2001, Veerman et al., 2005). In areas where demographic, ecological, environmental, epidemiological, health and socioeconomic data are sparse, these factors are anticipated to be highly heterogeneous. This quantitative documentation gap hampers long-term M&E activities (Eisenberg et

Acknowledgments

This paper is dedicated to Aliou Bah, a wonderful man with an amazing disposition and dedication for community engagement and field work, who sadly passed away during the cross-sectional survey conducted in one of the sentinel sites. Thanks are addressed to the baseline health survey team for their outstanding contribution, all the study participants for their commitment, Frédéric Chenais and Catherine Garcia from Rio Tinto Simandou project for the collaboration, and the national and local

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