Geosimulation approach for filling the gap of non‐response smoking data from the census 2013: A spatial analysis of census area unit geographies

Abstract

New Zealand has a goal of becoming a smoke‐free nation by 2025, with the aim of reducing smoking prevalence 5% or less. While the 2013 census provides good coverage about smoking prevalence, 9.3% of the population did not return valid responses. The aim of this article is to use the tool, spatial microsimulation, to estimate the missing non‐response data based on demographics and income at the census area unit (CAU) level to provide a more complete picture of the New Zealand smoking landscape. Results show that, including estimated non‐response data, only two CAUs meet the current smoke‐free goal.

Small-Area Estimation and Analysis of HIV/AIDS Indicators for Precise Geographical Targeting of Health Interventions in Nigeria. A Spatial Microsimulation Approach

“…Furthermore, SAE of HIV/AIDS indicators are yet to bene t from the immense analytical power of SMS, which typically requires rich data that are scarce in LMICs (7,8). Nevertheless, this has been employed in the analysis of other health phenomena, including smoking, obesity, mental illness, alcohol consumption, diabetes, and healthcare access, especially in developed countries, which do not experience the same data limitations as in the present study context (10)(11)(12)(13)(14)(15). Similar indicators and data have been used by other studies, except that these are not disaggregated at small-area scales like in the present study (16)(17)(18)(19)(20).…”