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Table 2 Bayesian multivariable local municipality mortality risk factor analysis, South Africa, 2007

From: A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa

Indicator

Bivariate analyses (unadjusted)

Multivariable model (adjusted)

 

Standard Poisson model i

BYM CAR iispatial Poisson model

 

RR (95% CI)

p-value

RR (95% BCI iii)

p-value

Composite service delivery index score iv

2.90 (1.99,4.20)

<0.001

1.84 (1.43,2.34)

<0.001

High local municipality income inequality

1.23 (1.04,1.46)

0.015

1.14 (1.02,1.29)

0.024

Low-medium district population density

1

   

 High density vi – non-metropolitan

1.13 (1.00,1.28)

0.050

0.97 (0.87,1.09)

0.648

 High density - metropolitan municipality

0.66 (0.52,0.83)

<0.001

0.73 (0.65,0.82)

<0.001

District antenatal HIV sero-prevalence

1.02 (1.01,1.03)

<0.001

1.02 (1.02,1.03)

<0.001

  1. i: robust standard errors to adjust for local municipality “cluster”.
  2. ii: incorporated an unstructured local municipality random effect and a structured normal CAR spatial random effect.
  3. iii: Bayesian credibility interval.
  4. iv: increasing score indicates increasingly poor service delivery (square transformation used due to violation of linear assumption in Poisson framework).
  5. v: lower tertiale.
  6. vi: upper quartile and adjusting for metropolitan.
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International Journal of Health Geographics

ISSN: 1476-072X