Table 4.

Comparing sign and P values of main exposure variables from 3-step Heckman Selection model vs. logistic regression models in Chinese adults aged 18–75 years, CHNS^a

	Hypertension prevalence		Hypertension diagnosis		Hypertension treatment
	With correction	Without correction	With correction	Without correction	With correction	Without correction
Female	<0.0001 (−b)	<0.0001 (−)	<0.01 (+)	<0.0001 (+)	0.11 (+)	<0.01 (+)
Education
Primary	0.30 (−)	0.17 (−)	<0.01 (+)	0.01 (+)	0.11 (+)	0.15 (+)
Secondary	<0.01 (−)	<0.01 (−)	<0.01 (+)	<0.01 (+)	0.04 (+)	0.09 (+)
>Secondary	<0.0001 (−)	<0.0001 (−)	<0.01 (+)	<0.001 (+)	0.11 (+)	0.13 (+)
Health insurance	0.35 (−)	0.20 (+)	0.32 (−)	0.17 (+)	0.12 (+)	0.03 (+)
Health insurance * time	0.08 (+)	0.40 (+)	0.11 (+)	0.35 (−)	0.36 (−)	0.17 (−)

Abbreviation: CHNS, China Health and Nutrition Survey.

^aThree-step Heckman selection model for sequential estimation of hypertension prevalence, hypertension diagnosis, and hypertension treatment had 5 points of support, nonlinear error terms, and clustering at the individual and household levels. Models additionally controlled for the confounders. The model for hypertension treatment controlled for age, household income, and community-level: communication, population density, educational and income diversity, health infrastructure, social services, transportation infrastructure, price of hypertension medication, and clustered at the community level. Model for hypertension diagnosis controlled for the same variables, except for price of hypertension medication. Model for hypertension prevalence controlled for the same variables as hypertension diagnosis, with additional control for region, and community level: housing infrastructure, traditional markets, modern markets, and sanitation infrastructure.

^bA single plus and minus denotes the sign of the beta coefficient from the regression model.