DEMOGRAPHIC

AND

HEALTH

RESEARCH

DHS WORKING PAPERS

2010 No. 78

Olalekan A. Uthman

December 2010

This document was produced for review by the United States Agency for 

International Development.

Does It Really Matter Where You Live? 

A Multilevel Analysis of Social Disorganization and 

Risky Sexual Behaviours in Sub-Saharan Africa
The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support 

provided by the United States Agency for International Development (USAID) through the MEASURE 

DHS project (#GPO-C-00-08-00008-00). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, 

MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; internet: 

www.measuredhs.com).
Does It Really Matter Where You Live? A Multilevel Analysis of Social Disorganization 

and Risky Sexual Behaviours in Sub-Saharan Africa 

Olalekan A. Uthman 

ICF Macro

Calverton, Maryland, USA

December 2010 

Corresponding author: Olalekan A. Uthman. Department of Public Health Sciences, Division of Social Medicine, 

Epidemiology, Karolinska Institutet, Stockholm, Sweden; E-mail: uthlekan@yahoo.com
ACKNOWLEDGEMENT

The data used in this study were made available through MEASURE DHS Archive. The 

data were collected under the MEASURE DHS project. Uthman AO was awarded a short-term 

DHS fellowship for population and health, funded by the U.S. Agency for International 

Development (USAID). The author acknowledges the support provided by Vinod Mishra, Astou 

Coly, Simona Bignami, Shanxiao Wang, and Sunita Kishor. 

Suggested citation:

Uthman, Olalekan A. 2010. Does It Really Matter Where You Live? A Multilevel Analysis of 

Social Disorganization and Risky Sexual Behaviours in Sub-Saharan Africa. DHS Working 

Papers No. 78. Calverton, Maryland, USA: ICF Macro. 
ABSTRACT

Background: The aim of this study is to examine individual and contextual factors associated 

with high-risk sexual behaviour in sub-Saharan Africa. Heterosexual relationships represent the 

major route of HIV/AIDS infection in Africa. Thus, understanding sexual behaviour is an 

essential step toward any effort to reduce the spread of the HIV/AIDS epidemic.

Methods: We applied multilevel logistic regression analyses on Demographic and Health 

Survey (DHS) data for 262,727 respondents (level 1) nested with 10,914 communities (level 2) 

from 26 sub-Saharan countries (level 3). 

Results: In all 26 countries studied, men were significantly more likely than women to have 

reported premarital sex. Except for Congo and Ethiopia, men were also significantly more likely 

to have reported non-spousal sex. At the community and country levels, there was statistically 

significant clustering of reported high-risk sexual behaviour. The following individual factors 

were associated with higher odds of reporting premarital and non-spousal sex: male gender, 

higher educational attainment and higher wealth status. The following contextual factors were 

associated with higher odds of reporting premarital and non-spousal sex: ethnic diversity, urban 

residence and small household size. 

Conclusion: We found that community and societal measures of social disorganization are 

important predictors of high-risk sexual behaviour. Thus, interventions aimed at reducing highrisk sexual behaviour should be implemented not only at the level of the individual but also at 

the community and societal levels. 

1

BACKGROUND 

Sub-Saharan Africa remains the region most heavily affected by HIV, accounting for 

67% of all people living with HIV and for 75% of AIDS deaths in 2007 (UNAIDS, 2008). At the 

end of 2007, an estimated 22 million adults and children in sub-Saharan Africa were living with 

HIV (UNAIDS, 2008). During that year, an estimated 1.5 million Africans died from AIDS 

(UNAIDS, 2008). The epidemic has left behind some 11.6 million orphaned African children 

(UNAIDS, 2008). 

While the nature of the causes and transmission of HIV/AIDS is complicated due to 

many biological, social, cultural and economic factors, the HIV/AIDS crisis is to a large extent a 

crisis of sexual behaviour (Lema et al., 2008). High-risk sexual behaviour, including exposure to 

multiple partners, increases the risks for HIV/AIDS and other sexual transmitted infections 

(STIs), and STIs facilitate the transmission of HIV (Bollen et al., 2008, Cohen, 2004, Da Ros and 

Schmitt Cda, 2008, Galvin and Cohen, 2004). Risky sexual behaviours can lead to serious health 

consequences both for the person involved and for any number of unseen partners. 

The prevalence of high-risk sexual behaviour in sub-Saharan Africa is a major public 

health concern, mostly because of the increasing incidence of HIV/AIDS (Djamba, 2003). About 

9 in 10 young people age 15–19 in Sub-Saharan Africa have heard of HIV/AIDS, but most are 

not familiar with the ―ABCs‖ of prevention: abstinence, being faithful (monogamy) and condom 

use (Bankole et al., 2004). Many adolescents, especially in rural areas, do not know where to 

obtain condoms (Bankole et al., 2004). 

Focusing on the individual alone ignores the broader social context within which sexual 

behaviours occur (Bajos, 1997). Many studies have been undertaken to understand factors 

associated with risky sexual behaviours. A small number of them using multilevel analyses have 

shown that social and community-level factors are associated with risky sexual behaviour 

(Benefo, 2008, Uthman, 2008, Uthman and Kongnyuy, 2008). Neighbourhoods constitute a key 

determinant of health, as they shape individual opportunities and expose residents to multiple 

risks and resources over the life course (Leventhal and Brooks-Gunn, 2000, Sampson, 2003). 
2

Conceptual Model 

This analysis draws upon social disorganization theory (Shaw and McKay, 1942). This 

framework presents at a high level how neighbourhoods directly and indirectly influence highrisk sexual behaviours (see Figure 1). This framework conceptualizes high-risk sexual behaviour 

as a multifaceted phenomenon grounded in the interplay of individual, family, community and 

societal factors. The model takes into account measures of social disorganization and their role in 

influencing high-risk sexual behaviour. Social disorganization identifies neighbourhood poverty, 

residential instability, family disruption, population density, and proximity to urban areas as key 

structural factors that diminish community-level self-regulatory capacity (Shaw and McKay, 

1942). 

The social disorganization thesis argues that communities with strong informal social 

networks are able to monitor and regulate sexual behaviour (Benefo, 2008). Consequently, 

structural factors that increase the complexity of community social organization and undermine 

informal social networks expand the range of sexual behaviours pursued by residents (Bishai et 

al., 2006, Browning and Olinger-Wilbon, 2003). Poverty reduces the resources necessary to 

sustain basic institutions like the family and organizations in neighbourhoods (Browning, 2002). 

Social disorganization theory hypothesises that the disruptive effects of immigration, 

industrialization and urbanization lead to changes in the social structure of neighbourhoods via 

ethnic diversity, residential instability and neighbourhood poverty. The resultant structural 

changes diminish the social cohesion of neighbourhoods and reduce the power of social norms 

and informal social control to regulate deviant behaviour. This can result in sexual HIV risk 

behaviours. The theory proposes that high ethnic diversity gives rise to social isolation. This in 

turn leads to structural barriers and cultural adaptations that undermine social organization. 

Shaw and McKay (1942) also traced social disorganization to conditions endemic to the 

urban areas that were the only places the newly arriving poor could afford to live, especially a 

high rate of turnover in the population (residential instability). These high levels of residential 

turnover can disrupt existing social networks. Urbanization has been found to be negatively 

associated with the coherence of normative environment (Billy and Moore, 1992). Increasing 

urbanization may give rise to an environment facilitating higher levels of sexual activity by 

creating greater anonymity, which lowers the risk of being "found out" (Billy and Moore, 1992). 
3

Non-traditional family structures, such as female-headed households, have been linked to social 

disorganization. The effect of social disorganization has been pointed out by research conducted 

on Zambian men’s non-spousal sex (Benefo, 2008), and other research has found that Ugandan 

men in ethnically heterogeneous communities are more likely to report non-spousal sex (Bishai 

et al., 2006). 

Figure 1. Conceptual model for determinants of high-risk sexual behaviour. 

To the best of our knowledge, there has been no multilevel study to date that examined 

the separate and independent association of individual, neighbourhood and country factors 

associated with risky sexual behaviour. Thus, the aim of this study is to answer the following 
4

research questions: 

1. Do neighbourhoods and countries differ in high-risk sexual behaviours? 

2. Are neighbourhood-level and country-level measures of social disorganization 

associated with high-risk sexual behaviours, after adjustment for individual-level 

variables? 

Answers to these questions can inform and help policymakers to consider appropriate 

policy options to reduce the prevalence of high-risk sexual behaviours in sub-Saharan Africa. 

Furthermore, because heterosexual relationships are the major route of HIV/AIDS infection in 

Africa, understanding high-risk sexual behaviour is an essential step toward reducing the spread 

of the HIV/AIDS epidemic (Djamba, 2003). 
5

SUBJECTS AND METHODS

Data 

This study used data from 26 Demographic and Health Surveys (DHS) conducted 

between 2003 and 2008 in sub-Saharan Africa (Benin, Burkina Faso, Cameroon, Chad, Congo, 

Democratic Republic of Congo, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, 

Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Swaziland, Tanzania, 

Uganda, Zambia and Zimbabwe)—the most recent available as of November 2009. Methods and 

data collection procedures have been published elsewhere (MEASURE DHS, 2009). Selection of 

the countries in this study was determined by availability of comparable data on sexual 

behaviour. In the DHS, interviewers administered a standardized questionnaire to participants in 

each country, yielding comparable data across countries. Country-level data were collected from 

the reports published by the United Nations Development Programs (UNDP, 2009). 

Definitions 

In our study, the term ―community‖ describes clustering within the same geographical 

living environment. Communities were based on sharing a common primary sample unit (PSU) 

within the DHS data. The DHS sampling frame for identifying PSUs is usually the most recent 

census. In urban areas, this results in census enumeration blocks being identified for sampling 

purposes. In rural areas, village areas are normally used to identify a PSU. Where a village is 

identified as having less than 50 households, it is normally joined with a larger neighbouring 

village to ensure that there are at least 50 households in each PSU. If a village has more than 500 

households, it is normally still only viewed as one PSU, although it will be segmented, with a 

sub-sample of the segments selected for household listing and interviewing. We used the terms 

―neighbourhood‖ and ―community‖ interchangeably in this study. 

Our study chose the PSU as the unit of analysis for two reasons. First, the PSU is the 

most consistent measure of community across all the DHS surveys (Harries, 1995), and thus the 

most appropriate identifier of community for this cross-region comparison. Second, it has been 

shown that for most of the DHS conducted to date, the sample size per cluster met the optimum 
6

size with a tolerable precision loss (Galvin and Cohen, 2004) (The bias introduced by using 

cluster averages based on about 25 women as a proxy for the PSU population averages is very 

small—only about 4% (Browning, 2002)). 

Ethical Consideration 

This study is based on an analysis of existing survey data with all identifier information 

removed. The survey was approved by the Institutional Review Board of ICF Macro in 

Calverton, Maryland, USA, and in some cases by country-specific ethics-related committees. All 

study participants gave informed consent before participation and all information was collected 

confidentially. 

Variables 

Outcome Variable 

Premarital sex and non-spousal sex were used as measures of risky sexual behaviours. 

For the present study, the analysis is limited to sexually experienced respondents who have been 

married at least once. The term ―premarital sex‖ was defined as having first sexual intercourse 

before age at first marriage. This was derived from a comparison of age at first intercourse and 

age at first marriage. Non-spousal sex was measured in the last 12 months, among women and 

men age 15-49 currently in union and who had sex in last 12 months. 

Explanatory Variables 

Individual-level factors: The following individual-level factors were included as control 

variables: Age of the respondent at the time of interview (15-24, 25-34, 35 or older); sex (male 

or female); education (no education, primary, secondary or higher); and polygamous (yes or no). 

The DHS did not collect direct information on household income and expenditure. We used the 

DHS wealth index as a proxy indicator for socioeconomic status. The methods used in 

calculating the DHS wealth index have been described elsewhere (Filmer and Pritchett, 2001, 
7

Montgomery et al., 2000, Vyas and Kumaranayake, 2006, Rustein and Johnson, 2004). Briefly, 

an index of economic status for each household was constructed using principal components 

analysis based on the following household variables: number of rooms per house; ownership of 

car, motorcycle, bicycle, fridge, television and telephone; and kind of heating device. From this 

information, the DHS wealth index quintiles (poorest, poor, middle, rich and richest) were 

calculated and were used in the subsequent modelling. 

Community-level factors: 

1. Neighbourhood poverty: percentage of households in the lowest quintile of the 

wealth index (Rustein and Johnson, 2004). 

2. Female-headed households: percentage of households headed by women in an area. 

3. Residential mobility: proportion of households occupied by persons who had moved 

from another dwelling in the previous five years (Osgood and Chambers, 2003, 

Sampson, 1985, Warner and Pierce, 1993). 

4. Place of residence: urban or rural, as administratively defined by each country. 

5. Population density: median household size in a community. 

6. Ethnic diversity: an index created using a formula (see equation 1, below) that 

captures both the number of different groups in an area and the relative representation 

of each group (Simpson, 1949): 

2

1

Ethnic diversity index = 1

n

i

i

x

 y

 

      (1)

where: 

i x

= population of ethnic group i of the area, 

y = total population of the area, and 

n = number of ethnic groups in the area 

Scores can range from 0 to approximately 1. The larger the index, the greater the 
8

diversity in an area. For clarity of interpretation, the score is multiplied by 100; if an area’s entire 

population belongs to one ethnic group, then an area has a diversity index of 0. An area’s 

diversity index increases to 100 if the population is evenly divided into ethnic groups. 

Country-level factors: The country-level variables include human poverty index (HPI), country 

population size and percentage urban areas in a country. HPI uses indicators of the most basic 

dimensions of deprivation: a short life, lack of basic education and lack of access to public and 

private resources. 

Statistical Analyses 

Descriptive Analyses 

In the descriptive statistics, the distributions of respondents by the key variables were 

expressed as percentages. We used Pearson's chi-squared test for analyzing contingency tables. 

All cases in the DHS data were given weights to adjust for differences in probability of selection 

and to adjust for non-response. Pooled sample weights were used for descriptive statistics in this 

study, using Stata 11 for Windows (StataCorp, 2009).

Modelling Approaches 

We specified a three-level multilevel model for our two binary outcomes (premarital and 

non-spousal sex) that had a structure of an individual (level 1) living in a community (level 2) 

within a country (level 3). We constructed four models. The first model, an empty or 

unconditional model without any exposure variables, was specified to decompose the amount of 

variance that existed between community and country levels. The second model contained 

individual-level variables: age and educational attainment. The third model was extended to 

include community-level variables. The fourth model additionally contained the country-level 

variable. 
9

Fixed Effects (Measures of Association) 

The results of fixed effects (measures of association) were shown as odds ratios (ORs) 

with their 95% confidence intervals (CIs). 

Random Effects (Measures of Variation) 

Measures of random effects included an intra-cluster correlation (ICC) and a variance 

partition coefficient (VPC). The ICC was calculated by the linear threshold according to the 

formula used by Snijders and Bosker (Snijders and Bosker, 1999). 

Model Fit and Specifications 

Regression diagnostics were used to judge the goodness-of-fit of the model. They 

included the tolerance test for multicollinearity, its reciprocal variance inflation factors (VIF) (Tu 

et al., 2004, Tu et al., 2005), presence of outliers and estimates of adjusted R square of the 

regression model. The largest VIF greater than 10 or the mean VIF greater than 6 represent 

severe multicollinearity (Hocking, 1996). Regression estimates were calculated by means of the 

re-weighted iterative generalised least square algorithm using MLwiN 2.20 (Rasbash et al., 

2008). In the multilevel logistic regression models, second order penalized quazi-likelihood 

(PQL) estimation was used (Goldstein, 2003). The statistical significance of covariates were 

calculated using the Wald test (Rasbash et al., 2008). All significance tests were two-tailed and 

statistical significance was defined at the 5% alpha level.
10

RESULTS

Sample Characteristics 

Table 1 lists the countries, year of data collection, final sample and number of 

communities sampled per country. As mentioned, the surveys were conducted between 2003 and 

2008. The median number of women and men sampled was 9,513 (range: 4,916 to 33,385) and 

3,838 (range: 1,887 to 15,486), respectively. The number of communities sampled ranged from 

196 to 886. The number of respondents per community ranged from 10 to 36. 

Table 1. Description of Demographic and Health Surveys data 2003-2008 in sub-Saharan Africa 

among men and women.

Country Survey year

Sample size

Number of 

communities

Median number of 

respondents per 

Women Men community

Benin 2006 17,794 5,321 750 21

Burkina Faso 2003 12,477 3,605 400 27

Cameroon 2004 10,656 5,280 466 21

Chad 2004 6,085 1,887 196 31

Congo 2005 7,051 3,146 225 29

Congo DR 2007 9,995 4,757 300 33

Ethiopia 2005 14,070 6,033 535 24

Ghana 2008 4,916 4,568 411 14

Guinea 2005 7,954 3,174 295 27

Kenya 2003 8,195 3,578 400 18

Lesotho 2004 7,095 2,797 405 13

Liberia 2007 7,092 6,009 298 28

Madagascar 2004 7,949 2,432 300 25

Malawi 2004 11,703 3,261 521 21

Mali 2006 14,583 4,207 407 36

Mozambique 2003 12,418 2,900 604 19

Namibia 2007 9,804 3,915 500 10

Niger 2006 9,223 3,549 342 28

Nigeria 2008 33,385 15,486 886 34

Rwanda 2005 11,321 4,820 462 20

Senegal 2005 14,602 3,761 376 31

Swaziland 2006 4,987 4,156 275 12

Tanzania 2004 10,329 2,635 475 19

Uganda 2006 8,531 2,503 368 21

Zambia 2007 7,146 6,500 319 26

Zimbabwe 2005 8,907 7,175 398 24
11

Figure 2 shows percentages of respondents who reported premarital sex. The percentages 

vary widely by country. The percentage of men who reported premarital sex ranged from 24% in 

Niger to 90% in Congo. The percentage of women who reported premarital sex ranged from 5% 

in Niger to 79% in Congo. In all 26 countries, men were significantly more likely than women to 

have reported premarital sex. In Senegal and Guinea men were more than 10 times more likely to 

have reported premarital sex than were women. 
Figure 2. Gender differences and percentages of respondents who reported premarital sex (ranked by odds ratio). 

12
13

Figure 3 shows percentages of respondents who reported non-spousal sex. The 

percentage of men who reported non-spousal sex ranged from 2% in Ethiopia to 68% in Congo. 

The percentage of women who reported non-spousal sex ranged from 1% in Niger to 67% in 

Congo. Except for Congo and Ethiopia, men were significantly more likely than women to have 

reported non-spousal sex. Men from Ethiopia, however, were less likely than women to have 

reported non-spousal sex. There was no statistically significant difference between men and 

women in Congo. 
Figure 3. Gender differences and percentages of respondents who reported non-spousal sex (ranked by odds ratio). 

14
15

Table 2 presents descriptive statistics for the final pooled sample. For this analysis, 

information on 262,727 respondents (level 1) nested with 10,914 communities (level 2) from the 

26 sub-Saharan countries (level 3) was pooled into one data set. Most of the respondents were 

women (67%), and most respondents lived in rural areas (67%). Respondents were fairly evenly 

divided across the age, education and wealth strata. More than half of the respondents were in 

polygamous relationships (55%). The median community household size was 6.3 persons. The 

median neighbourhood poverty level was 5.9%, while 21.4% of households were female-headed, 

and residential instability was 17.9%. The median community ethnic diversity index was 0.0. At 

the country level, the median human poverty index was 36.2, median population size was 14.4 

million, and the median percentage urban was 35.2%. 
16

Table 2. Summary statistics of sample.

Variable Percentage*/ Median (IQR)

LEVEL 1: INDIVIDUAL (n=262,727) Percentage

Sex

Male 33.2

Female 66.8

Age (years)

15-24 37.3

25-34 27.1

35+ 35.6

Educational attainment

No education 21.5

Primary 37.2

Secondary or higher 41.3

Polygamous

Yes 67.2

No 32.8

Wealth status

Poorest 17.1

Poor 18.1

Middle 19.1

Rich 21.2

Richest 24.5

LEVEL 2: COMMUNITIES (n=10,914) Median (IQR)

Ethnic diversity 0.0 (43.0)

Neighbourhood poverty 5.9 (33.3)

Female-headed households 21.4 (26.0)

Residential instability 17.9 (27.8)

Place of residence (%)

Rural 67.0

Urban 33.1

Household size 6.3 (2.5)

LEVEL 3: COUNTRIES (n=26) Median (IQR)

Human poverty index 36.2 (16.0)

Population (millions) 14.4 (28.2)

Percentage urban population 35.4 (20.7)

IQR: Interquantile range

*

Pooled sample weights were applied
17

Premarital Sex 

Table 3 shows fixed-effect (measures of association) and random-effect (measures of 

variation) results from multilevel analysis with premarital sex as the outcome variable. The 

results of the empty model showed that approximately 23% of the variance in the log odds of 

reporting premarital sex could be attributed to the community level (



=0.369, p<0.0001) and 

14% to the country level (



=0.607, p<0.0001). The variations across communities and countries 

remained statistically significant even after controlling for individual-level, community-level, 

and country-level factors (Table 4), thereby lending support for the use of multilevel modelling 

to account for community and country variations. 

Table 3 also shows results of fitting the model including individual-level, communitylevel, and country-level factors (Model 4). With all factors controlled for statistically, men were 

274% more likely to have reported premarital sex (adjusted OR [aOR]=3.74; 95% CI 3.65 to 

3.84). Respondents age 35 or older were more likely to have reported premarital sex (aOR=1.13; 

95% CI 1.10 to 1.15) than were respondents age 15-24. The odds of reporting premarital sex 

declined with decreasing education and decreasing wealth status. Compared with respondents 

with secondary or higher education, respondents with no education were less likely to have 

reported premarital sex (aOR=0.57; 95% CI 0.55 to 0.58). Respondents from the poorest 

households were less likely to have reported premarital sex than those from the richest 

households (aOR=0.87; 95% CI 0.83 to 0.91). The association between premarital sex and 

polygamous relationship was not significant. 

Respondents from communities with high ethnic diversity (aOR=1.08; 95% CI 1.04 to 

1.12), high residential mobility (aOR=1.08; 95% CI 1.05 to 1.12) and high proportions of 

female-headed households (aOR=1.14; 95% CI 1.10 to 1.18) were more likely to have reported 

premarital sex. Respondents from neighbourhoods with high levels of poverty were 14% less 

likely to have reported premarital sex (aOR=0.86; 95% CI 0.83 to 0.90). Compared with rural 

areas, respondents from urban areas were more likely to have reported premarital sex 

(aOR=1.10; 95% CI 1.05 to 1.14). The odds of reporting premarital sex decreased as household 

size increased (aOR = 0.94; 95% CI 0.91 to 0.98). For each one-unit increase in a country's 

human poverty index score, the odds of reporting premarital sex decreased by 4% (aOR=0.96; 

95% CI 0.93 to 0.98). The association between reported premarital sex and a country's 

population size and percentage urban was not statistically significant. 
18

Table 3. Measures of variations and factors associated with premarital sex identified by multilevel 

logistic regression models.

Variable Model 1a Model 2b Model 3c Model 4d

LEVEL 1: INDIVIDUAL

Female (versus male) 3.31 (3.23 to 3.39)*** 3.47 (3.38 to 3.56)*** 3.74 (3.65 to 3.84)***

Age (years)

15-24 1.00 (reference) 1.00 (reference) 1.00 (reference)

25-34 1.24 (1.21 to 1.27)*** 1.24 (1.21 to 1.27)*** 1.25 (1.23 to 1.28)***

35+ 1.12 (1.09 to 1.15)*** 1.12 (1.09 to 1.15)*** 1.13 (1.10 to 1.15)***

Educational attainment

No education 0.56 (0.55 to 0.58)*** 0.58 (0.56 to 0.59)*** 0.57 (0.55 to 0.58)***

Primary 0.76 (0.74 to 0.78)*** 0.76 (0.74 to 0.78)*** 0.76 (0.74 to 0.77)***

Secondary or higher 1.00 (reference) 1.00 (reference) 1.00 (reference)

Polygamous (yes vs. no) 0.98 (0.96 to 1.00) 0.98 (0.96 to 0.99)*0.98 (0.96 to 1.00)

Wealth status

Poorest 0.77 (0.74 to 0.80)*** 0.87 (0.83 to 0.91)*** 0.87 (0.83 to 0.91)***

Poor 0.81 (0.78 to 0.83)*** 0.88 (0.85 to 0.92)*** 0.88 (0.85 to 0.92)***

Middle 0.83 (0.80 to 0.85)*** 0.88 (0.85 to 0.91)*** 0.88 (0.85 to 0.91)***

Rich 0.87 (0.85 to 0.90)*** 0.90 (0.88 to 0.93)*** 0.90 (0.87 to 0.93)***

Richest 1.00 (reference) 1.00 (reference) 1.00 (reference)

LEVEL 2: COMMUNITIES 

Ethnic diversity 1.08 (1.04 to 1.12)*** 1.08 (1.04 to 1.12)***

Neighbourhood poverty 0.86 (0.84 to 0.89)*** 0.86 (0.83 to 0.90)***

Residential mobility 1.06 (1.03 to 1.09)*** 1.08 (1.05 to 1.12)***

Female-headed households (%) 1.13 (1.10 to 1.16)*** 1.14 (1.10 to 1.18)***

Urban (vs. rural) area 1.08 (1.04 to 1.12)*** 1.10 (1.05 to 1.14)***

Household size 0.93 (0.90 to 0.96)*** 0.94 (0.91 to 0.98)**

LEVEL 3: COUNTRIES 

Human poverty index 0.96 (0.93 to 0.98)***

Population (millions) 1.00 (0.99 to 1.00)

Percentage urban population 1.02 (1.00 to 1.03)

Random effects

Country-level

Variance (SE) 0.607 (0.169) 0.504 (0.140) 0.484 (0.135) 0.271 (0.075)

ICC 14.2 12.2 11.7 6.8

Community-level

Variance (SE) 0.369 (0.08) 0.346 (0.007) 0.342 (0.007) 0.422 (0.009)

ICC 22.9 20.5 20.1 17.4

aModel 1 is the empty model, a baseline model without any exposure variable (N= 261668)

bModel 2 is adjusted for sex, age, education, polygamous, and wealth (N= 244952)

cModel 3 is additionally adjusted for community-level factors (N= 244952)

dModel 4 is additionally adjusted for country-level factors (N= 244952)

Abbreviations: SE: standard error; ICC: intra-cluster correlation coefficient

***p < 0.001, **p < 0.01, and *p < 0.05 
19

Non-Spousal Sex 

Table 4 shows fixed-effect (measures of association) and random-effect (measures of 

variation) results from multilevel analysis with non-spousal sex as the outcome variable. The 

results of the empty model showed that approximately 32% of the variance in the log odds of 

reporting non-spousal sex could be attributed to the community level (



=0.599, p=0.004) and 

20% to the country level (



=0.982, p<0.0001). As with the premarital sex outcome, the 

variations in the non-spousal sex outcome across communities and countries remained 

statistically significant after controlling for individual-level, community-level, and country-level 

factors (Table 4), lending support for the use of multilevel modelling to account for community 

and country variations. 

Table 4 also shows results of fitting the model including individual-level, communitylevel, and country-level factors (Model 4). With all factors controlled for statistically, men were 

16% more likely to have reported non-spousal sex (adjusted OR [aOR]=1.16; 95% CI 1.11 to 

1.21). Respondents age 35 or older were less likely to have reported non-spousal sex (aOR=0.55; 

95% CI 0.53 to 0.57) than were respondents age 15-24. The odds of reporting non-spousal sex 

decreased with decreasing education and decreasing wealth status. Compared with respondents 

with secondary or higher education, respondents with no education were less likely to have 

reported non-spousal sex (aOR=0.71; 95% CI 0.68 to 0.74). Respondents from the poorest 

households were less likely to have reported non-spousal sex than those from the richest 

households (aOR=). Polygamous respondents were more likely to report non-spousal sex 

(aOR=2.49; 95% CI 2.42 to 2.56). The association between non-spousal sex and wealth index 

was not statistically significant. 
20

Table 4. Measures of variations and factors associated with non-spousal sex identified by 

multilevel logistic regression models.

Variable Model 1a Model 2b Model 3c Model 4d

LEVEL 1: INDIVIDUAL

Female (versus male) 1.07 (1.03 to 1.12)*** 1.18 (1.13 to 1.23)*** 1.16 (1.11 to 1.21)***

Age (years)

15-24 1.00 (reference) 1.00 (reference) 1.00 (reference)

25-34 0.75 (0.73 to 0.78)*** 0.75 (0.73 to 0.77)*** 0.75 (0.73 to 0.78)***

35+ 0.55 (0.53 to 0.57)*** 0.54 (0.52 to 0.56)*** 0.55 (0.53 to 0.57)***

Educational attainment

No education 0.65 (0.63 to 0.68)*** 0.68 (0.66 to 0.71)*** 0.71 (0.68 to 0.74)***

Primary 0.86 (0.83 to 0.89)*** 0.88 (0.85 to 0.91)*** 0.89 (0.86 to 0.92)***

Secondary or higher 1.00 (reference) 1.00 (reference) 1.00 (reference)

Polygamous (yes vs. no) 2.55 (2.47 to 2.63)*** 2.54 (2.47 to 2.63)*** 2.49 (2.42 to 2.56)***

Wealth status

Poorest 0.79 (0.75 to 0.83)*** 0.99 (0.93 to 1.05) 1.00 (0.94 to 1.06)

Poor 0.85 (0.81 to 0.89)*** 1.02 (0.97 to 1.07) 1.02 (0.97 to 1.08)

Middle 0.90 (0.86 to 0.94)*** 1.03 (0.98 to 1.08) 1.03 (0.98 to 1.08)

Rich 0.96 (0.92 to 0.99)*1.04 (0.99 to 1.08) 1.03 (0.99 to 1.08)

Richest 1.00 (reference) 1.00 (reference) 1.00 (reference)

LEVEL 2: COMMUNITIES 

Ethnic diversity 1.06 (1.01 to 1.12)*1.08 (1.02 to 1.14)**

Neighbourhood poverty 0.91 (0.86 to 0.95)*** 0.91 (0.86 to 0.96)***

Residential mobility 1.04 (0.99 to 1.08) 1.05 (1.00 to 1.09)*

Female-headed 

households (%) 1.29 (1.23 to 1.35)*** 1.28 (1.22 to 1.34)***

Urban (vs. rural) area 1.27 (1.21 to 1.34)*** 1.29 (1.23 to 1.36)***

Household size 0.94 (0.89 to 0.99)*0.94 (0.89 to 0.99)*

LEVEL 3: COUNTRIES 

Human poverty index 0.99 (0.95 to 1.02)

Population (millions) 1.01 (0.99 to 1.02)

Percentage urban 

population 1.02 (0.99 to 1.05)

Random effects

Country-level

Variance (SE) 0.982 (0.279) 1.027 (0.292) 0.974 (0.276) 0.809 (0.230)

ICC 20.2 20.4 19.3 16.4

Community-level

Variance (SE) 0.599 (0.013) 0.712 (0.015) 0.780 (0.015) 0.839 (0.016)

ICC 32.4 34.6 34.8 33.4

aModel 1 is empty model, baseline model without any exposure variable (N= 244940)

bModel 2 is adjusted for sex, age, education, polygamous, and wealth (N= 244924)

cModel 3 is additionally adjusted for community-level factors (N= 244924)

dModel 4 is additionally adjusted for country’-level factors (N= 244924)

Abbreviation: SE: standard error; ICC: intra-cluster correlation coefficient

***p < 0.001 **p < 0.01, and *p < 0.05
21

Respondents from communities with high ethnic diversity (aOR=1.08; 95% CI 1.02 to 

1.14) and high levels of female-headed households (aOR=1.28; 95% CI 1.22 to 1.34) were more 

likely to have reported non-spousal sex. Compared with rural areas, respondents from urban 

areas were more likely to have reported non-spousal sex (aOR=1.29; 95% CI 1.23 to 1.36). 

Respondents from areas of high neighbourhood poverty were 9% less likely to have reported 

non-spousal sex (aOR=0.91; 95% CI 0.86 to 0.96). The odds of reporting non-spousal sex 

decreased with increasing household size (aOR=0.94; 95% CI 0.89 to 0.99). The association 

between residential instability and reported non-spousal sex was not statistically significant. The 

association between levels of non-spousal sex and the country's human poverty index, population 

size, and percentage urban population were not statistically significant. 
22

DISCUSSION 

Using multilevel analysis, we found that community and societal measures of social 

disorganization are important predictors of high-risk sexual behaviours in the sub-Saharan 

countries studied. These associations have been reported in previous studies from United States 

on short-term partnering (Browning and Olinger-Wilbon, 2003), as well as on adolescent sexual 

activity and contraceptive use (Billy et al., 1994, Billy and Moore, 1992, Brewster, 1993, 

Brewster, 1994). The association between social disorganization and high-risk sexual intercourse 

has also been documented in sub-Saharan Africa non-spousal (Benefo, 2008, Bishai et al., 2006). 

We found that men were more likely than women to report high-risk sexual behaviour. 

This is in agreement with the results of previous studies that have examined the association 

between sex of the respondent and likelihood of reporting high-risk sexual behaviours (Bennetts 

et al., 1999, Ndinya-Achola et al., 1997, Olayinka et al., 2000, Thomas et al., 2009, Wang et al., 

2007). For example, Ndinya-Achola and co-researchers analysed sexual behaviours of young 

adults who were attending a primary health care clinic in Nairobi, Kenya, and found that men 

were more likely than women to report having two or more partners during the past year 

(Ndinya-Achola et al., 1997). 

Our multilevel random intercept models allow us to disentangle individual, community, 

and societal variation in reported high-risk sexual behaviour. We found that people living in the 

same neighbourhood tend to have similar behaviour. This is in part because people in the same 

neighbourhood are subject to common contextual influences. This contextual phenomenon 

expresses itself as clustering of individual attitudes within neighbourhood. That is, a portion of 

the health differences among people may be attributable to the areas in which they reside (Merlo 

et al., 2005). On these grounds, we might conclude that there is some evidence for a possible 

neighbourhood and country contextual phenomenon shaping high-risk sexual behaviours; and 

that neighbourhoods are very important in understanding individual difference in high-risk 

sexual behaviours. This indicates that policy and public health preventive services that operate 

on relatively large geographical and population-based scales are potential intervention points and 

should be considered in conjunction with health programs that focus on individual sexual 

behaviour. 
23

Our findings should be considered in light of the following limitations: First, we did not 

measure the length of time that participants had spent in their neighbourhoods and the extent of 

their exposure to the neighbourhood environment. We were thus unable to determine whether 

associations of neighbourhood characteristics with high-risk sexual behaviours were due to 

cumulated effects. Second, another important limitation is that our data were based on selfreported sexual activity. By definition, self-reports of initiation of sexual activity cannot be 

externally validated, and studies have revealed considerable inconsistencies in self-reported 

sexual activity (Lauritsen and Swicegood, 1997, Meekers, 1995). Third, the cross-sectional 

nature of the data limits ability to draw casual inferences from the associations found. 

Despite these limitations, the study’s strengths are significant. It is a large, populationbased study with national coverage from 26 countries with high response rates. Overall, the 

number of included countries and geographic and socioeconomic diversities constitute a good 

yardstick for the sub-Saharan region and help to strengthen the findings from the study. The 

DHS have some important advantages when compared with other surveys. Not only are they 

nationally representative, but also they include the same variables in the same way and thus 

make it possible to compare data across countries. Beyond communities, individuals are 

influenced by national policies which affect the proximate determinants of high-risk sexual 

behaviours. Understanding the relative contribution of individual, community, and societal 

factors is important for policymakers in order to design and carry out public health interventions. 
24

CONCLUSION 

Drawing upon the multilevel perspective, this study presented an approach to examining 

premarital and non-spousal sex. The existence of community and societal factors influencing 

high-risk sexual behaviours underscores the need to implement public health prevention 

strategies not only at the individual level but also tailored to the community context of the 

population they are aiming to protect. Future studies should investigate other factors that may 

account for the unexplained neighbourhood and country variations in high-risk sexual 

behaviours. Future research also should address the mechanisms that connect the individual and 

neighbourhood levels—that is, the means through which deleterious neighbourhood effects are 

transmitted to the residents. 
25

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