| Literature DB >> 29562899 |
Miriam Harter1, Sebastian Mosch2, Hans-Joachim Mosler2.
Abstract
BACKGROUND: Community-led total sanitation (CLTS) is a widely used, community-based approach to tackle open defecation and its health-related problems. Although CLTS has been shown to be successful in previous studies, little is known about how CLTS works. We used a cross-sectional case study to identify personal, physical, and social context factors and psychosocial determinants from the Risks, Attitudes, Norms, Abilities, and Self-Regulation (RANAS) model of behavior change, which are crucial for latrine ownership and analyze how participation in CLTS is associated with those determinants.Entities:
Keywords: Behavior change; CLTS; Psychosocial determinants; RANAS; Social context
Mesh:
Year: 2018 PMID: 29562899 PMCID: PMC5861600 DOI: 10.1186/s12889-018-5287-y
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Quantity of CLTS-related information
| Group label | Quantity of CLTS information received | CLTS intervention |
|---|---|---|
| A | Participation | Receivers |
| B | No participation/information received | Receivers |
| C | No participation/no information received | Non-receivers |
| D | No CLTS village | Non-receivers |
Personal, Physical, and Social Context Factors
| Personal context | Physical context | Social context |
|---|---|---|
| Age | Risk of flooding | Social dilemma |
| Relationship status | Soil conditions | Social capital |
| Years at school | Social identity | |
| Ability to read/ write | Social cohesion | |
| Religion | ||
| Household size | ||
| Average monthly income/family |
Fig. 1Differences in latrine ownership status related to the extent of which people received CLTS-related information. a) households which participated in CLTS (=participation), n = 131; b) households which did not attend the CLTS intervention in their community personally but received CLTS-related information indirectly from relatives, friends, and neighbors (=no participation/ information received), n = 177; c) households in communities that underwent a CLTS triggering event but which did not receive any CLTS-related information (=no participation/ no information received), n = 170 and finally d) households in control communities where CLTS was not performed (=no CLTS village), n = 125
Predictors of Latrine Ownership in Logistic Regression Analysis
| Model |
|
| Wald X2 (1) | OR | 95% CI |
|---|---|---|---|---|---|
| Model 4: significant context and RANAS factors from model 1 + 2 + 3 | |||||
| Context factors | |||||
| Relationship statusa | .545 | .388 | 1.969 | .58 | .27, 1.24 |
| Years at school | .188 | .070 | 7.247** | 1.21 | 1.05, 1.39 |
| Risk of flooding | −.351 | .128 | 7.546** | .70 | .55, .90 |
| Social dilemma | .046 | .131 | .123 | 1.04 | .81, 1.35 |
| Social capital (solidarity) | .110 | .093 | 1.411 | 1.12 | .93, 1.34 |
| Social capital (trust) | −.080 | .103 | .602 | .92 | .75, 1.13 |
| Social capital (social cohesion and inclusion) | .377 | .119 | 10.068** | 1.46 | 1.16, 1.84 |
| RANAS factors | |||||
| Vulnerability (personal general risk for diarrhea) | −.626 | .113 | 30.734*** | .54 | .43, .67 |
| Feeling more respected | −.381 | .141 | 7.327** | .68 | .52, .90 |
| Beliefs about costs and benefits (money, space, time) | −1.143 | .267 | 18.246*** | .32 | .19, .54 |
| Others’ behavior (community) | 1.176 | .141 | 69.105*** | 3.24 | 2.46, 4.28 |
| Others’ (dis)approval (personally important others’) | .544 | .161 | 11.479** | 1.72 | 1.26, 2.36 |
| Confidence in recovery of broken latrine | .994 | .199 | 25.029*** | 2.70 | 1.83, 3.99 |
| Communication | .155 | .136 | 1.297 | 1.17 | .89, 1.52 |
| Constant | −8.13 | 1.62 | 25.381*** | ||
Note. N = 598. For the overall model of significant context and psychosocial factors (Model 4) R2 = .74 (Nagelkerke). X2(15) = 468.19, p < .0005. Latrine ownership was coded ‘1’ and no latrine ownership was coded ‘0’.
aNo relationship as reference category;
OR = odds ratio; CI = confidence interval; **P < .005; ***P < .0005
Fig. 2Statistical diagram of the multiple mediation model for the indirect influence of CLTS on latrine ownership through several social and psychosocial context factors. CLTS received was coded ‘1’ and CLTS not received was coded ‘0’. Latrine ownership was coded ‘1’, and no latrine ownership was coded ‘0’. a1 - a7 = unstandardized regression coefficients from linear regressions; b1 – b7 = unstandardized regression coefficients from logistic regression; c’ = indirect effect of CLTS on latrine ownership status
Summary of multiple mediation analysis: CLTS indirectly influencing latrine ownership status through its effect on several social and psychosocial factors
| Mediator | CLTS | Latrine ownership | Indirect effect (95% CI) | Odds ratio for specific indirect effects (95% CI) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|
| LL |
| UL | OR | |
| Social capital | 0.402*** | 0.109 | .000 | 0.298** | 0.108 | .005 | 0.028 |
| 0.267 | 1.127 |
| Vulnerability | −.593*** | 0.115 | −0.577*** | 0.103 | .000 | 0.174 |
| 0.555 | 1.409 | |
| Feelings | .026 | 0.090 | .770 | −0.327** | 0.127 | .009 | −0.087 | − 0.009 | 0.052 | 0.99 |
| Beliefs about costs and benefits | −.194*** | 0.050 | .000 | −1.339*** | 0.249 | .000 | 0.113 |
| 0.472 | 1.297 |
| Others’ behavior | 1.110*** | 0.101 | .000 | 1.082*** | 0.126 | .000 | 0.856 |
| 1.582 | 3.327 |
| Others’ (dis)approval | .517*** | 0.082 | .000 | 0.521*** | 0.148 | .000 | 0.096 |
| 0.483 | 1.309 |
| Confidence in recovery of broken latrine | .260** | 0.083 | .002 | 0.910*** | .177 | .000 | 0.074 |
| 0.447 | 1.267 |
Note N = 593. B = unstandardized regression coefficients from linear regressions (CLTS) and logistic regression (latrine ownership); SE = standard error; CI = confidence interval for specific indirect effects; LL = lower limit; UL = upper limit; OR = odds ratio for specific indirect effects
CLTS received was coded ‘1’, and CLTS not received was coded ‘0’. Latrine ownership was coded ‘1’, and no latrine ownership was coded ‘0’. Bias-corrected bootstrap confidence intervals for the specific indirect effects were computed based on 10,000 bootstrap samples (bold: Significant effects)