Table 2.
Household food insecurity explained by three-level multinomial hierarchical linear models, Mexico, 2014
| Model | Severe food insecurity | Moderate food insecurity | Mild food insecurity | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Coefficient | OR | 95 % CI | Coefficient | OR | 95 % CI | Coefficient | OR | 95 % CI | |
| Model 1 | |||||||||
| Intercept | −4·041 | 0·018 | 0·014, 0·022 | −3·165 | 0·042 | 0·033, 0·054 | −2·035 | 0·131 | 0·103, 0·166 |
| Municipality level | |||||||||
| Disasters | 0·458 | 1·581 | 1·284, 1·945 | 0·323 | 1·382 | 1·144, 1·671 | 0·198 | 1·220 | 1·050, 1·417 |
| Density | −0·295 | 0·744 | 0·693, 0·799 | −0·258 | 0·772 | 0·725, 0·823 | −0·274 | 0·760 | 0·722, 0·801 |
| Model 2 | |||||||||
| Intercept | −3·926 | 0·019 | 0·017, 0·023 | −3·038 | 0·048 | 0·042, 0·055 | −1·9277 | 0·145 | 0·127, 0·167 |
| Municipality level | |||||||||
| Disasters | 0·253 | 1·287 | 1·066, 1·555 | 0·109 | 1·115 | 0·941, 1·323 | 0·015 | 1·015 | 0·883, 1·168 |
| Poverty quintiles | 0·410 | 1·507 | 1·419, 1 ·600 | 0·388 | 1·474 | 1·398, 1·555 | 0·354 | 1·425 | 1·362, 1·490 |
| Model 3 | |||||||||
| Intercept | −4·087 | 0·017 | 0·014, 0·020 | −3·224 | 0·040 | 0·032, 0·049 | −2·131 | 0·119 | 0·097, 0·145 |
| Municipality level | |||||||||
| Disasters | 0·453 | 1·573 | 1·281, 1·932 | 0·318 | 1·374 | 1·138, 1·661 | 0·197 | 1·218 | 1·051, 1·412 |
| Density | −0·264 | 0·768 | 0·719, 0·820 | −0·235 | 0·791 | 0·744, 0·841 | −0·263 | 0·768 | 0·732, 0·807 |
| State level | |||||||||
| Food programmes | −0·034 | 0·967 | 0·918, 1·018 | −0·020 | 0·980 | 0·918, 1·046 | 0·006 | 1·006 | 0·943, 1·073 |
| Change in power | 0·022 | 1·022 | 0·789, 1·325 | 0·039 | 1·039 | 0·755, 1·430 | 0·091 | 1·095 | 0·800, 1·498 |
| Per capita GDP quintiles | −0·277 | 0·758 | 0·692, 0·831 | −0·276 | 0·758 | 0·677, 0·850 | −0·248 | 0·780 | 0·697, 0·873 |
| Model 4 | |||||||||
| Intercept | −3·911 | 0·020 | 0·017, 0·023 | −3·036 | 0·048 | 0·041, 0·056 | −1·971 | 0·139 | 0·120, 0·161 |
| Municipality level | |||||||||
| Disasters | 0·287 | 1·333 | 1·105, 1·608 | 0·139 | 1·149 | 0·969, 1·362 | 0·047 | 1·048 | 0·913, 1·203 |
| Poverty quintiles | 0·374 | 1·453 | 1·366, 1·546 | 0·358 | 1·430 | 1·354, 1·512 | 0·324 | 1·383 | 1·323, 1·446 |
| State level | |||||||||
| Food programmes | −0·051 | 0·951 | 0·909, 0·994 | −0·037 | 0·963 | 0·922, 1·006 | −0·019 | 0·981 | 0·938, 1·027 |
| Change in power | −0·059 | 0·942 | 0·752, 1·180 | −0·057 | 0·945 | 0·759, 1·177 | 0·019 | 1·019 | 0·814, 1·275 |
| Per capita GDP quintiles | −0·138 | 0·871 | 0·800, 0·948 | −0·143 | 0·867 | 0·799, 0·941 | −0·135 | 0·874 | 0·804, 0·950 |
GDP, gross domestic product.
All models were estimated with household-level covariates: woman as head of household, education of head of household, household size, presence of older adults (70 years or older = 1) and presence of children (5 years or younger = 1); estimates not shown, but available in the online supplementary material. The dependent variable was always food insecurity, estimated using the Latin American and Caribbean Food Security Scale (ELCSA) from the National Household Income and Expenditure Survey (ENIGH) 2014, and the reference category was ‘food security’. Models 1 and 2 focused on the municipality level and Models 3 and 4 on the state level, all adjusting for household-level covariates. Model 1 estimated the effects of population density and vulnerability to disasters. Model 2 substituted population density by a more complex composite index of poverty. Model 3 added to Model 1 the three state-level variables: the number of nutrition programmes, change in political party and per capita GDP. Likewise, Model 4 added the same variables to Model 2.