Assistive products and the Sustainable Development Goals (SDGs).

The Sustainable Development Goals (SDGs) have placed great emphasis on the need for much greater social inclusion, and on making deliberate efforts to reach marginalized groups. People with disabilities are often marginalized through their lack of access to a range of services and opportunities. Assistive products can help people overcome impairments and barriers enabling them to be active, participating and productive members of society. Assistive products are vital for people with disabilities, frailty and chronic illnesses; and for those with mental health problems, and gradual cognitive and physical decline characteristic of aging populations. This paper illustrates how the achievement of each of the 17 SDGs can be facilitated by the use of assistive products. Without promoting the availability of assistive products the SDGs cannot be achieved equitably. We highlight how assistive products can be considered as both a mediator and a moderator of SDG achievement. We also briefly describe how the Global Cooperation on Assistive Technology (GATE) is working to promote greater access to assistive products on a global scale.

Using DHS and MICS data to complement or replace NGO baseline health data: an exploratory study.

Background: Non-government organizations (NGOs) spend substantial time and resources collecting baseline data in order to plan and implement health interventions with marginalized populations. Typically interviews with households, often mothers, take over an hour, placing a burden on the respondents. Meanwhile, estimates of numerous health and social indicators in many countries already exist in publicly available datasets, such as the Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS), and it is worth considering whether these could serve as estimates of baseline conditions. The objective of this study was to compare indicator estimates from non-governmental organizations (NGO) health projects' baseline reports with estimates calculated using the Demographic and Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS), matching for location, year, and season of data collection. Methods: We extracted estimates of 129 indicators from 46 NGO baseline reports, 25 DHS datasets and three MICS datasets, generating 1,996 pairs of matched DHS/MICS and NGO indicators. We subtracted NGO from DHS/MICS estimates to yield difference and absolute difference, exploring differences by indicator. We partitioned variance of the differences by geographical level, year, and season using ANOVA. Results: Differences between NGO and DHS/MICS estimates were large for many indicators but 33% fell within 5% of one another. Differences were smaller for indicators with prevalence <15% or >85%. Difference between estimates increased with increasing year and geographical level differences. However, <1% of the variance of the differences was explained by year, geographical level, and season. Conclusions: There are situations where publicly available data could complement NGO baseline survey data, most importantly when the NGO has tolerance for estimates of low or unknown accuracy.