DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2016 No. 127

Gertrude Chapotera

Vinitha Jayachandran

John Phuka

August 2016

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

International Development.

Factors Associated with HIV Infection 

among Educated Malawians: 

Analysis of the 2010 Demographic and Health Survey

Factors Associated with HIV Infection 

among Educated Malawians: 

Analysis of the 2010 Demographic and Health Survey 

Gertrude Chapotera1

Vinitha Jayachandran1

John Phuka1 

ICF International 

Rockville, Maryland, USA 

August 2016 

1

 Department of Public Health, University of Malawi, Blantyre, Malawi 

Corresponding author: John Phuka, Department of Public Health, University of Malawi; 

jphuka@medcol.mw.
Acknowledgments 

We acknowledge the United States Agency for International Development (USAID) for funding 

our research project through the DHS Fellows Program implemented by ICF International. We 

would like to thank our facilitators, Wenjuan Wang, Shireen Assaf, Damian Jeremia Damian, 

Simona Simona, and Elizabeth Nansubuga, and our reviewers, Sarah Staveteig and Lia Florey, for 

their valuable comments. We appreciate the support of all 2016 DHS Fellows. We also thank the 

Principal of the College of Medicine, University of Malawi, and the Dean of the School of Public 

Health and Family Medicine for allowing us time to participate in the DHS Fellows Program. 

Editor: Bryant Robey 

Document Production: Natalie La Roche 

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 DHS Program (#AIDOAA-C-13-00095). The views expressed are those of the 

authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and 

evaluate population, health, and nutrition programs. For additional information about The DHS 

Program, contact DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 

20850, USA. Phone: +1 301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; 

Internet: www.dhsprogram.com. 

Recommended citation: 

Chapotera, Gertrude, Vinitha Jayachandran, and John Phuka. 2016. Factors Associated with HIV 

Infection among Educated Malawians: Analysis of the 2010 Demographic and Health Survey. 

DHS Working Papers No. 127. Rockville, Maryland, USA: ICF International.
i 

Abstract 

Introduction: Among people who have ever attended school, higher educational attainment 

appears to be associated with higher prevalence of HIV. This study assesses the association 

between education and HIV status in Malawi, among individuals with some education, after 

adjusting for various background characteristics. 

Methods: We used nationally representative data from the 2010 Malawi Demographic and Health 

Survey (MDHS). A weighted total of 5,092 women age 15-49 and 5,460 men age 15-54 who were 

tested for HIV and ever had sex were included in the analysis. Multiple logistic regressions were 

used to explore the association between HIV status and educational attainment, as well as other 

factors. 

Results: Among women with a secondary education, compared with women with a primary 

education, the unadjusted odds ratio of testing HIV-positive was 1.6. Men with a secondary 

education had 1.1 higher odds of testing HIV-positive compared with men with a primary 

education. Among women with a tertiary education, compared with women with a primary 

education, the odds ratio was 1.5, and among men it was 1.7. The association of HIV prevalence 

with education, however, was no longer significant in the models controlling for variables related 

to high-risk behaviors, access to health care services, and partner and demographic characteristics. 

Adjusted models showed significantly increased odds of HIV associated with a higher lifetime 

number of partners, marriage status, comprehensive knowledge of HIV/AIDS among men but not 

women, older age, urban residence, residence in the Southern Region of Malawi, higher wealth 

status, higher occupation rank, and among women but not men. 

Conclusion: Education was not found to be associated with being HIV-positive, but lifetime 

number of sexual partners, age, residence, and wealth status may be important factors associated 

with HIV seropositivity. Except for lifetime number of sexual partners and marital history, the 

other factors that are most likely to be associated with HIV seropositivity reflect survival rates 

related to knowledge and access to care. Individuals with higher education may live longer with 

HIV infection than individuals with lower educational attainment, resulting in higher HIV 

prevalence among the educated, even if their infection rates are not as high. Our findings perhaps 

suggest a need for better education on HIV/AIDS and better health practices, while further 

supporting avoidance of risky behavior. 

Key words: Educational attainment, HIV, Malawi 

1 

1. Introduction 

HIV Epidemiology in Malawi 

Malawi is among the countries worst affected by the HIV pandemic. In Malawi, the number of 

people living with HIV and AIDS (PLHIV) was estimated to be over one million in 2014, which 

includes 930,000 people age 15 and older, and 130,000 children under age 14 (UNAIDS 2016). 

Sixty percent of seropositive adults are women. In 2014, the prevalence of HIV was estimated at 

10%, and the number of new cases was estimated at 34,000 per year, including 26,000 new 

infections among people age 15 and older, and 7,400 new infections among children under age 15 

(National AIDS Commission 2015). 

After the first case of HIV was diagnosed in 1981, the number of people affected rose until peaking 

in 1999. From then, both the incidence and prevalence of HIV in Malawi have been declining, 

largely due to reduction in risky behavior (Bello et al. 2011). Slowing of the incidence of HIV 

resulted in a reduction of HIV prevalence, as smaller proportions of the population were newly 

infected. Another determinant of prevalence of HIV in Malawi is longer survival of HIV patients. 

Since 2004, Malawi scaled up Antiretroviral Therapy (ART), which has influenced the trajectory 

of the HIV epidemic by reducing mortality, morbidity, and transmission (Government of Malawi 

2015). In addition, seropositive individuals have greater access to improved care through 

prophylaxis treatment of infection, improved management, and nutritional support. Between 2010 

and 2015, ART services averted an estimated 30,000-35,000 deaths each year (UNAIDS 2016). 

Although there has been reduction in incidence over time, more patients now survive for longer, 

and HIV remains an important disease with a high burden on the Malawian society. 

The 2010 Malawi Demographic and Health Survey (DHS) shows that prevalence of HIV varies 

substantially with gender, age, urban-rural residence, geographic area, and socioeconomic status. 

Among adults age 15-49, women have higher HIV prevalence than men (13% compared with 8%). 

Gender and age seem to interact, with the age distribution of HIV prevalence varying between 

women and men. Adolescent and young women in Malawi have four times higher HIV prevalence 

than men in the same age groups (National Statistical Office (NSO) and ICF Macro 2011). This 

difference has also been observed elsewhere. For example, in South Africa in 2014 adolescent and 

young women had eight times higher risk than men in the same age groups (Dellar, Dlamini, and 

Karim 2015). Factors considered to determine this pattern, both in Malawi and South Africa, 

include but are not limited to intergenerational sex, social vulnerability, and biological 

vulnerability of young women. In Malawi, however, men are affected more in older ages, over age 

35, and also the cumulative lifetime number of sex partners is correlated strongly with HIV 

prevalence among men. Other factors presumed to affect HIV prevalence among men include 
2 

increasing income with age, which enables men to pay for transactional sex and to have multiple 

concurrent sexual relationships. 

Distribution of prevalence of HIV in Malawi also varies by residence and region of the country. 

HIV prevalence is twice as high in urban areas (17%) compared with rural areas (9%). Among the 

three regions of Malawi, HIV prevalence is highest in the Southern Region, at 14.5%, double that 

in the Northern Region and Central Region. Within these broad geographical regions, variations 

also exist by district, sub-district, proximity to main roads, and socio-demographic groups 

(National Statistical Office (NSO) and ICF Macro 2011). 

Association of HIV Prevalence and Educational Attainment 

Educational status is well recognised as a positive determinant of health. The World Health 

Organization (WHO) lists education as one of the key determinants of health and explains that 

higher education levels are linked with better health outcomes (World Health Organization 2016). 

A recent review of the Sustainable Development Goals (SDGs) showed that education leverages 

health outcomes (Vladimirova and Le Blanc 2015). 

Recent Demographic and Health Surveys (DHS) in Malawi show that HIV prevalence in Malawi 

varies by educational attainment but with a bimodal distribution—that is, prevalence is highest 

among those with no education, while among those who ever attended school, HIV prevalence 

increases with the level of education attained (National Statistical Office (NSO) and ICF Macro 

2011; (National Statistical Office (NSO) [Malawi] and ORC Macro 2005). This observation has 

also been made in other countries (Hargreaves and Glynn 2002; Smith et al. 1999; Fortson 2008). 

Factors affecting HIV prevalence among those who are educated may be different from those 

among individuals without education. These factors therefore need to be investigated separately. 

In this study, we examined factors associated with educational attainment and HIV prevalence 

only among those who ever attended school, because of the potential to influence policy for 

targeted interventions in schools. Our study conceptualizes that attainment of higher education is 

associated with either an increase in risky behaviors or an increase in survival rates. More risky 

behavior probably is due to better access to disposable income, increased travel, and increased 

time away from home that may increase the lifetime number of sexual partners. Survival could 

also be related to education in that higher educational attainment promotes better access to and use 

of health care services (National Statistical Office (NSO) and ICF Macro 2011). 

Study Rationale 

An increasing burden of HIV with increasing educational level works against the goals of investing 

in education. The substantial investment required by the country to educate Malawians, especially 
3 

at the tertiary level, may not yield the expected returns to education because of the dangers posed 

by HIV/AIDS. Therefore, we need prevention measures that protect the school-going young 

population from primary HIV infection. Schools and academic institutions provide a logical venue 

for interventions targeting this high-risk population. In Malawi, however, academic institutions 

and schools lack a strong public health policy to back school-based HIV prevention interventions, 

such as condom promotion and distribution. Such interventions are widely considered in Malawi 

to promote sexuality among children, in a society with cultural and religious values that only accept 

sex after marriage, and therefore are rarely implemented. As intervention is clearly needed, and as 

schools are a conducive environment for targeted protection of young adolescents, youths, and 

young adults who are attaining an education, it is critical that other solutions be explored. Further 

examination of factors influencing HIV infection and survival may help to explain the paradoxical 

association of educational attainment and HIV prevalence, among those who have received any 

schooling, and therefore to better understand which interventions may be most effective in 

reaching Malawians who have attended school. 

One of the paradigm shifts in the National HIV Prevention Strategy 2015-2020 in Malawi is a 

focus on targeted interventions, in contrast to blanket coverage. Our study is complementary to 

this prevention strategy, as it examines prevalence of HIV among a specific population. Through 

the National HIV Prevention Strategy 2015-2020, Malawi seeks to complement the global effort 

that integrates treatment and prevention—“90-90-90: The Ambitious Treatment Target to Help 

End the AIDS Epidemic” (UNAIDS 2014). This study aims to provide evidence toward targeted 

solutions that will have local and global impact on reducing prevalence of HIV. 

Conceptual Framework 

Based on the literature regarding risk factors for HIV infection, we created a conceptual framework 

to guide our analysis (Figure 1). The conceptual framework has two levels of associated factors 

that determine prevalence of HIV among educated individuals: distal and proximal factors. Distal 

factors are the underlying or contextual factors that influence proximal factors. In addition to 

educational attainment, other distal factors include socioeconomic status (measured using the DHS 

wealth index), work status, and demographic characteristics such as age, sex, marital status, tribe, 

and religion. 

Higher educational status is associated with higher income in Malawi (Castel, Phiri, and Stampini 

2010). Those with higher education are more likely to have the means to pay for sex and to have 

a greater lifetime number of sexual partners. Similarly, higher wealth, working status, and older 

age may increase exposure to a higher lifetime number of partners. Women are biologically 

vulnerable to HIV infection, and younger age puts them at higher risk of intergenerational sex, 

which is an independent risk factor (Dellar, Dlamini, and Karim 2015; Tenkorang 2014). Religion 
4 

and tribe may influence age at sexual debut and risk of multiple sexual partners. Some tribes in 

Malawi are known to have values that promote risky sexual behavior through cultural rituals 

(Peters, Kambewa, and Walker 2010). However, educational attainment may improve individuals’ 

assertiveness to refuse participation in high-risk behaviors. In addition, more highly educated 

seropositive individuals may be more likely to seek care and to have access to treatment such as 

ART compared with those with less education. Although HIV care is although offered free in 

Malawi, it has cost implications mainly related to transportation (Pinto et al. 2013). Therefore, 

individuals with higher education may live longer with HIV infection than those with lower 

educational attainment. This may result in higher HIV prevalence rates among the educated, even 

if their infection rates are not as high.

Figure 1. Association between HIV status and educational level: A conceptual framework 
5 

2. Methods 

2.1. Data 

We used nationally representative data from the 2010 Malawi Demographic and Health Survey 

(MDHS). The 2010 MDHS used a two-stage clustering sample design and selected 849 clusters 

with a total of 27,345 households. A subsample of one-third of the households was selected for 

HIV testing among eligible women age 15-49 and eligible men age 15-54. Only those tested for 

HIV during the survey were selected in the first stage of our study sample selection. Our study 

primarily aims to provide information that will support targeted interventions in schools, and hence 

the analysis focused on people who ever attended school and excluded those who never attended 

school. In addition, we focused on women and men who were ever exposed to sexual activities, 

because sexual transmission is the primary source of HIV infection in Malawi; hence only women 

and men who attended school and ever had sex were included in the study sample. Figure 2 shows 

the derivation of the analysis sample. 

Figure 2. Derivation of the analysis sample 

 

All

30195

Men

7175

No HIV Test

338

HIV Tested

6837

No Education

449

Educated

6388

Never had Sex

928

Ever had Sex

5460

Women

23020

No HIV Test

15930

HIV Tested

7090

No Education

1096

Educated

5994

Never had Sex

902

Ever had Sex

5092
6 

2.2. Key Variables 

For our analysis the outcome variable was HIV serostatus. The independent variables included 

eight distal factors: educational attainment, wealth index, work status, age, marital status, sex, 

tribe, and religion; and included nine proximal factors: lifetime number of sex partners, consistent 

condom use, age of sexual debut, paying for sex, exposure to media, HIV/AIDS comprehensive 

knowledge, knowledge on source of condom, access to services, and staying away from home. We 

analysed educational attainment as a categorical variable but explored its performance when 

modelled as a continuous variable. 

All of our measures were simple binary or categorical variables defined as response categories in 

the DHS questionnaire, except for the variable on comprehensive knowledge on HIV/AIDS. This 

variable is a binary (yes/no) variable defined as knowing that using a condom consistently during 

sexual intercourse and having just one uninfected faithful partner can reduce the chance of getting 

the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the 

two most common misconceptions in Malawi about AIDS transmission or prevention (i.e. 

knowing correctly that mosquitoes do not transmit HIV and that one cannot get HIV through 

witchcraft) (National Statistical Office (NSO) and ICF Macro 2011). 

A variable on access to ART services was generated at the DHS cluster level using data from the 

Malawi Service Provision Assessment 2013-14 database(Ministry of Health (MoH) [Malawi] and 

ICF International 2014). GPS data from both DHS and SPA surveys were used to identify facilities 

with ART services within a 10-km buffer for rural clusters, and a 5-km buffer for urban clusters. 

Levels of access to ART services were defined as low if there was no facility with ART services 

within the respective buffer, medium if there was one facility with ART services, and high if there 

were two or more facilities with ART services within the buffer. 

2.3. Statistical Analysis 

We conducted the analysis in several steps. First, we explored the outcome and explanatory 

variables through descriptive analyses. We studied the variables in the different domains of our 

conceptual framework, namely socioeconomic and demographic characteristics, high-risk sexual 

behavior, knowledge of HIV, access to media, access to ART services, and urban-rural residence. 

We ran bivariate analyses of HIV serostatus with educational attainment as well as other factors, 

stratifying by sex. Finally, we ran multiple logistic regression analyses that had HIV serostatus as 

the outcome and educational attainment as the main predictor variable, controlling for age, age at 

sex debut, wealth index, access to ART services, religion, ethnicity, mass media exposure, 

consistent condom use, comprehensive HIV knowledge, and access to ART services. The 
7 

collinearity among the selected variables in the conceptual framework was checked before 

conducting the regressions. 

STATA 14 was used in data management and analysis. Sample weight was applied to correct for 

non-response and sampling disproportionalities, and the analysis accounted for the effect of 

complex survey design. Odds ratios and their corresponding 95% confidence intervals were 

reported to determine independent association of the variables with HIV prevalence. Statistical 

significance level was set at p<0.05. 

9 

3. Results 

3.1. Sample Description 

A weighted sample of 5,092 women and 5,460 men who attended at least one year of schooling 

and ever had sex were included in the analysis. Table 1 summarizes background characteristics of 

women and men in the sample. The majority of respondents among women (77%) and men (67%) 

had attained only primary school education, were rural residents (79% of women and 78% of men), 

and were married at the time of the survey (76% of women and 67% men). About a third of the 

population were youth under age 25. Access to ART services was good—77% of women and 78% 

of men resided near two or more facilities that provided ART services. 

Table 1. Percent distribution of women and men included in the analysis, by background 

characteristics 

 Women Men 

Background characteristics Number % Number % 

Education 

Primary 3,937 77.3 3,637 66.6 

Secondary 1,038 20.4 1,636 30.0 

Tertiary 117 2.3 186 3.4 

Age 

15-19 672 13.2 922 16.9 

20-24 1,193 23.4 991 18.2 

25-29 1,273 25.0 973 17.8 

30-34 752 14.8 816 14.9 

35-39 562 11.0 652 11.9 

40-44 352 6.9 447 8.2 

45-49 288 5.7 371 6.8 

50-54 n/a n/a 287 5.3 

Marital status 

Never married 450 8.8 1,624 29.7 

Currently married 3,882 76.2 3,628 66.5 

Formerly married 760 14.9 207 3.8 

Residence 

Rural 4,023 79.0 4,281 78.4 

Urban 1,069 21.0 1,179 21.6 

Region 

Northern 651 12.8 602 11.0 

Central 2,101 41.3 2,454 45.0 

Southern 2,340 45.9 2,403 44.0 

Wealth quintile 

Poorest 763 15.0 699 12.8 

Poorer 954 18.7 1.051 19.3 

Middle 998 19.6 1,122 20.6 

Richer 1,022 20.1 1,154 21.1 

Richest 1,355 26.6 1,433 26.2 

Access to ART services 

Low 230 4.6 245 4.6 

Medium 894 18.0 955 17.9 

High 3,851 77.4 4,146 77.6 

Total 5,092 100.0 5460 100.0 
10 

3.2. HIV Prevalence and Differentials by Educational Attainment and Other 

Background Characteristics 

HIV prevalence among women was 15 % compared with 9% among men. Figure 3 shows an 

increase in HIV prevalence with increased educational attainment, among both women and men. 

Figure 3. HIV prevalence by educational attainment among women and men, MDHS 2010 

Table 2 shows HIV status by other background characteristics. HIV seropositivity is higher with 

increased education, age, and wealth status, higher among the employed compared with the 

unemployed, among rural compared with urban residents, in the Southern Region, and almost 

double among the formerly married compared with the married or never-married. 

 

13.2

19.3 18.7

14.5

9.1 9.7

14.2

9.4

0

5

10

15

20

25

Primary Secondary Higher Total

Women Men
11 

Table 2. Percentage of women and men who were HIV-positive, by background characteristics, 

Malawi 2010 

Women Men

Characteristic % 95% CI N % 95% CI N 

Education 

Primary 13.2 [11.9,14.6] 3,937 9.1 [7.8,10.5] 3,637 

Secondary 19.3 [15.8,23.4] 1,038 9.7 [7.9,11.8] 1,636 

Tertiary 18.7 [11.4,29.2] 117 14.2 [9.4,20.9] 186 

Age 

15-19 6.3 [4.2,9.2] 672 0.8 [0.4,1.7] 922 

20-24 6.4 [4.8,8.4] 1,193 3.2 [2.0,5.3] 991 

25-29 13.6 [11.3,16.4] 1,273 6.9 [5.0,9.6] 973 

30-34 22.2 [18.6,26.3] 752 11.1 [8.5,14.5] 816 

35-39 25.5 [21.0,30.5] 562 18 [14.0,22.8] 652 

40-44 23.5 [18.6,29.4] 352 22.3 [17.3,28.4] 447 

45-49 19.4 [14.6,25.4] 288 14.9 [11.1,19.7] 371 

50-54 N/A N/A N/A 15.4 [10.4,22.1] 287 

Residence

Urban 26.3 [22.1,30.8] 1,069 15.1 [11.9,18.8] 1,179 

Rural 11.4 [10.3,12.7] 4,023 7.9 [6.9,9.0] 4,281 

Region 

Northern 9.3 [7.2,12.0] 651 6.5 [4.7,9.0] 602 

Central 9.9 [7.9,12.4] 2,101 7.3 [5.7,9.3] 2,454 

Southern 20.2 [18.1,22.4] 2,340 12.3 [10.7,14.3] 2,403 

Religion 

Christian 14.3 [12.9,15.9] 4,467 9.5 [8.3,10.9] 4,638 

Muslim 16.7 [13.2,21.0] 573 8.9 [6.4,12.1] 617 

No/Other religion 8.8 [2.9,23.5] 52 8.5 [3.8,18.0] 205 

Ethnicity 

Chewa 9.8 [8.0,12.0] 1,688 5.5 [4.1,7.3] 1,783 

Tumbuka 11.2 [8.1,15.2] 505 5.9 [3.8,9.0] 466 

Lomwe 22.8 [19.5,26.4] 883 14.5 [11.7,17.9] 1,035 

Yao 19 [14.9,24.1] 602 12.3 [9.3,16.0] 670 

Ngoni 14.8 [11.5,18.7] 677 8.8 [6.1,12.5] 715 

Wealth index 

Poorest 8.5 [6.4,11.2] 763 5.8 [4.0,8.2] 699 

Poorer 10.1 [8.2,12.5] 954 7.2 [5.4,9.6] 1,051 

Middle 11.9 [9.8,14.4] 998 8.5 [6.6,10.9] 1,122 

Richer 14.5 [11.8,17.6] 1,022 9.7 [7.5,12.4] 1,154 

Richest 23 [19.6,26.8] 1,357 13.3 [10.8,16.3] 1,433 

Marital status 

Never married 7.7 [5.2,11.3] 450 2.3 [1.5,3.5] 1,624 

Currently married 11.7 [10.3,13.4] 3,882 11.9 [10.4,13.6] 3,628

Formerly married 32.8 [28.8,37.1] 760 22.1 [16.0,29.7] 207 

Occupation 

Not employed 10.8 [8.3,14.0] 1,219 3.1 [1.7,5.6] 427 

Unskilled/Manual 11 [9.5,12.6] 2,368 7.8 [6.7,9.1] 2,931 

Clerical/Skilled 23.3 [20.5,26.4] 1,397 12.6 [10.6,14.8] 1,883 

Technical/Managerial 21.3 [13.4,32.1] 108 16.6 [10.5,25.3] 218 
12 

3.3. Logistic Regression Analysis of Factors Associated with HIV Serostatus 

Results of unadjusted logistic regressions 

Table 3 presents the unadjusted logistic regression results looking at factors associated with HIV 

serostatus among educated women and men. 

Table 3. Unadjusted logistic regression analysis of factors associated with HIV 

prevalence among educated Malawian women and men, MDHS 2010 

Women Men 

Variables OR 95% CI OR 95% CI 

Education 

Primary 1.0 - 1.0 - 

Secondary 1.6*** 1.2 - 2.0 1.1 0.8 - 1.4 

Tertiary 1.5 0.8 - 2.7 1.7* 1.0 - 2.7 

Age 

15-19 1.0 - 1.0 - 

20-24 1.0 0.6 - 1.7 3.9** 1.5 - 10.0 

25-29 2.4*** 1.5 - 3.7 8.8*** 3.8 - 20.6 

30-34 4.3*** 2.7 - 6.7 14.7*** 6.5 - 33.1 

35-39 5.1*** 3.1 - 8.4 26.9*** 11.6 - 62.3 

40-44 4.6*** 2.8 - 7.7 35.5*** 15.0 - 84.1 

45-49 3.6*** 2.2 - 5.9 22.0*** 9.2 - 52.4 

50-54 n/a n/a 23.3*** 9.5 - 56.8 

Residence 

Urban 1.0 - 1.0 - 

Rural 0.4*** 0.3 - 0.5 0.5*** 0.4 - 0.7 

Region 

Northern 1.0 - 1.0 - 

Central 1.1 0.7 - 1.6 1.1 0.7 - 1.7 

Southern 2.5*** 1.8 - 3.3 2.0*** 1.4 - 3.0 

Religion 

Catholic 1.0 - 1.0 - 

Muslim 1.2 0.9 - 1.6 0.9 0.6 - 1.3 

No/Other religion 0.6 0.2 - 1.9 0.9 0.4 - 2.1 

Ethnicity 

Chewa 1.0 - 1.0 - 

Tumbuka 1.2 0.8 - 1.7 1.1 0.6 - 1.9 

Lomwe 2.7*** 2.1 - 3.6 2.9*** 1.9 - 4.5 

Yao 2.2*** 1.5 - 3.1 2.4*** 1.6 - 3.8 

Ngoni 1.6* 1.1 - 2.3 1.7* 1.1 - 2.5 

Other 1.5* 1.1 - 2.0 2.3*** 1.5 - 3.6 

Occupation 

Not employed 1.0 - 1.0 - 

Unskilled/Manual 1.0 0.7 - 1.4 2.7** 1.4 - 5.0 

Clerical/Skilled 2.5*** 1.8 - 3.4 4.5*** 2.4 - 8.6 

Technical/Managerial 2.2* 1.2 - 4.2 6.2*** 2.7 - 14.4 

Continues 

 
13 

Table 3—Continued 

Women Men 

Variables OR 95% CI OR 95% CI 

Wealth quintile 

Poorest 1.0 - 1.0 - 

Poorer 1.2 0.8 - 1.8 1.3 0.8 - 2.0 

Middle 1.5* 1.0 - 2.1 1.5 0.9 - 2.4 

Richer 1.8** 1.2 - 2.7 1.7* 1.1 - 2.8 

Richest 3.2*** 2.2 - 4.6 2.5*** 1.6 - 3.9 

Marital status 

Never married 1.0 - 1.0 - 

Currently married 1.6* 1.0 - 2.5 5.6*** 3.6 - 8.8 

Formerly married 5.9*** 3.7 - 9.4 11.9*** 6.6 - 21.4 

Travel away from home in past 

12 months 

Did not travel away 1.0 - 1.0 - 

Away for more than one month in 

last 12 months 

1.2 0.9 - 1.7 0.9 0.6 - 1.3 

HIV knowledge 

Comprehensive knowledge score 1.4** 1.2 - 1.8 1.6*** 1.2 - 2.0 

Know source for male condoms 1.0 - 1.0 - 

Don't know source for male 

condoms 

0.7 0.5 - 1.0 0.6 0.3 - 1.1 

Know source for female condoms 1.0 - 1.0 - 

Don't know source for female 

condoms 

0.7** 0.6 - 0.9 0.9 0.7 - 1.1 

Access to ART services within 

the buffer 

No access to ART services 1.0 - 1.0 - 

Access to one facility with ART 2.2** 1.4 - 3.5 1.3 0.7 - 2.4 

Access to two or more facilities 

with ART 

2.5*** 1.6 - 3.9 1.4 0.8 - 2.4 

Access to radio (media) 

Less than once a week 1.0 - 1.0 - 

At least once a week 1.1 0.8 - 1.4 1.0 0.7 - 1.3 

Lifetime number of partners 

One partner 1.0 - 1.0 - 

Two partners 2.7*** 2.0 - 3.5 3.2*** 1.9 - 5.2 

Three partners 6.2*** 4.5 - 8.5 5.2*** 3.1 - 8.7 

Four or more partners 12.5*** 8.6 - 18.0 7.5*** 4.7 - 12.2 

Consistent condom use in past 

12 months 

No 1.0 - 1.0 - 

Yes 1.5 0.8 - 2.7 0.9 0.5 - 1.8 

Age at sexual debut 

<14 1.0 - 1.0 - 

14-18 0.9 0.6 - 1.2 1.5 0.9 - 2.4 

19-24 0.7 0.5 - 1.1 1.3 0.8 - 2.2 

>=25 1.1 0.7 - 1.7 1.4 0.8 - 2.6 

Weighted number 5,092 5,460 

*** p<0.001, ** p<0.01, * p<0.05 

The unadjusted odd ratio of testing HIV positive among women with secondary compared with 

primary education was 1.6 (95 % CI 1.2-2.0) and among men the odds ratio was 1.1 (95 % CI 0.8-

1.4) comparing the same groups. Comparing tertiary education with primary education, the odds 
14 

ratio was 1.5 (95 % CI 0.8-2.7) among women and 1.7 (95 % CI 1.0-2.7) among men. The odds 

comparing secondary education with primary education among women were significant, as were 

the odds comparing tertiary education with primary education among men. The odds of testing 

HIV-positive significantly increased with increasing age among both women and men, with a large 

magnitude of difference among men. There were no significant differences observed for religion, 

consistent condom use, age at sexual debut, access to radio, access to ART services, or travel away 

from home. 

Rural women and men had significant 60% and 50% lower odds of testing HIV-positive, 

respectively, when compared with urban women and men. Among both men and women, 

significantly higher odds were observed for residents of the Southern Region compared with the 

Northern Region. Odds were higher among current or formerly married individuals compared with 

unmarried individuals. Individuals in the richer and richest wealth quintiles had higher odds of 

testing HIV-positive compared with the poorest wealth quintile. Concerning employment, as Table 

3 shows, no significant difference in HIV prevalence was seen when comparing unskilled/manual 

labor with the unemployed, while the odds were significantly higher among the employed, whether 

clerical, skilled, or technical/managerial employment. 

Among women, significantly higher odds were observed for those with better access to ART 

services compared with those with no access (Figure 4). The odds of testing HIV-positive rose as 

the lifetime number of partners increased, among both women and men. 
15 

Figure 4. Unadjusted odds ratios of HIV seropositivity by access to ART services and lifetime 

number of partners among Malawian women and men, MDHS 2010 

Results of multivariate logistic regressions 

Table 4 shows the adjusted results of multivariable logistic regressions. The association between 

HIV prevalence and educational attainment observed in bivariate models disappeared in the 

multivariate models. Significant associations remained between HIV seropositivity and age, 

residence, region, marital status for the formerly married, and lifetime number of partners. Women 

with secondary school education had significant 60% higher odds of testing HIV positive 

compared with women with only primary school education. The odds reduced to 30% and were 

no longer significant when adjusted for wealth index, access to ART services, religion, ethnicity, 

access to radio, consistent condom use, and comprehensive HIV knowledge. Women with tertiary 

education had 50% higher odds than those with primary education, but this was not significant. 

Among men, significant odds were observed for men with tertiary education, at 70% higher odds, 

compared with primary education. 

 

1

2.2 2.5

1

2.7

6.2

12.5

1 1.3 1.4

1

3.2

5.2

7.5

0

2

4

6

8

10

12

14

No Access

to ART

services

Access to 1

facility with

ART

Access to

>=2

facilities

with ART

Lifetime

number of

partners

1 partner 2 partners 3 partners 4> =

partners

odds ratio

Women Men
16 

Table 4. Multivariate analysis of factors associated with HIV prevalence among Malawian women and men

Women Men 

Variable OR CI OR CI 

Highest level of education attained (Reference: primary) 

Secondary 1.3 1.0 - 1.9 0.9 0.7 - 1.3

Tertiary 0.8 0.4 - 1.8 1.0 0.4 - 2.3 

Age (Reference: 15-19) 

20-24 0.7 0.4 - 1.2 4.0* 1.4 - 11.6 

25-29 1.4 0.9 - 2.4 6.6*** 2.2 - 20.0

30-34 2.4** 1.4 - 4.1 10.2*** 3.4 - 30.7 

35-39 2.9*** 1.7 - 5.2 17.9*** 5.9 - 54.3

40-44 2.5** 1.4 - 4.4 24.0*** 7.9 - 73.3 

45-49 2.4** 1.3 - 4.4 15.6*** 4.9 - 49.9

50-54 - - 13.8*** 4.3 - 44.1 

Residence (Reference: urban) 

Rural 0.6** 0.4 - 0.8 0.6** 0.4 - 0.8 

Region (Reference: Northern) 

Central 1.1 0.7 - 1.8 1.5 0.9 - 2.7

Southern 1.9** 1.2 - 2.9 1.7* 1.0 - 2.7 

Religion (Reference: Catholic)

Muslim 1.0 0.6 - 1.8 0.5 0.2 - 0.9 

No/Other religion 1.0 0.3 - 3.1 0.8 0.4 - 1.9

Ethnicity (Reference: Chewa) 

Tumbuka 1.0 0.6 - 1.7 1.3 0.7 - 2.7 

Lomwe 1.4 0.9 - 2.1 2.5*** 1.4 - 4.2

Yao 1.2 0.6 - 2.3 3.3*** 1.6 - 6.6

Ngoni 0.9 0.6 - 1.5 1.5*** 0.9 - 2.4 

Other 0.9 0.6 - 1.4 2.1** 1.2 - 3.6

Occupation (Reference: not employed) 

Unskilled/Manual 1.0 0.7 - 1.4 0.9 0.4 - 1.8 

Clerical/Skilled 1.6** 1.2 - 2.3 0.9 0.4 - 1.9

Technical/Managerial 0.7 0.3 - 1.6 0.8 0.3 - 2.2 

Poorest (Reference: poorest) 

Poorer 1.2 0.8 - 1.9 1.0 0.6 - 1.7 

Middle 1.6* 1.1 - 2.3 1.2 0.7 - 2.0

Richer 1.5 1.0 - 2.2 1.1 0.7 - 1.8 

Richest 2.2** 1.4 - 3.5 1.5 0.9 - 2.6

Access to radio media (Reference: less than once a 

week)

At least once a week 1.1 0.8 - 1.3 0.9 0.6 - 1.2 

Consistent condom use in past 12 months (Reference: 

No)

 

Yes N/A N/A 0.9 0.6 - 1.4 

Marital Status (Reference: never married) 

Currently married 1.7 0.8 - 3.3 1.7 0.9 - 3.1 

Formerly married 4.0*** 2.0 - 8.3 3.5** 1.6 - 7.7

Lifetime number of partners (Reference: one partner) 

Two partners 2.0*** 1.5 - 2.6 2.0* 1.1 - 3.6

Three partners 3.8*** 2.8 - 5.3 2.9*** 1.6 - 5.0 

Four or more partners 6.8*** 4.5 - 0.2 3.6*** 2.0 - 6.5 

HIV Comprehensive knowledge score 1.1 0.9 - 1.4 1.4* 1.0 -1.8 

Access to ART services within the buffer (Reference: No 

access to ART services)

 

Access to one facility with ART services 1.7 0.9 - 3.4 1.2 0.7 - 2.2 

Access to two or more facilities with ART services 1.3 0.7 - 2.6 1.0 0.6 - 1.8

Weighted number 4964 5331 

*** p<0.001, ** p<0.01, * p<0.05 
17 

4. Discussion 

The most obvious finding to emerge from this study is that, after controlling for risk behaviors and 

social economic variables, educational attainment was no longer significantly associated with HIV 

status. Both among women and men, significant positive associations with HIV seropositivity were 

observed with lifetime number of sexual partners, marital status, wealth index, region, and age. 

Comprehensive HIV knowledge was associated with HIV seropositivity among men only. 

Our results agree with other studies demonstrating that risky behavior is the most important factor 

associated with prevalence of HIV. Our result, that HIV prevalence is associated with lifetime 

number of sex partners, is similar to findings from other analyses of DHS data and cohort studies. 

Further analysis of nationally representative surveys from Ethiopia DHS surveys in 2005 and 2011, 

the Malawi 2010 DHS, and the Ghana 2011 DHS show the lifetime number of sex partners to be 

one of the main predictors of HIV seropositivity (Kenyon, Tsoumanis, and Schwartz 2015; Akwara 

et al. 2005). Cohort studies in Tanzania and South Africa also have shown that HIV seropositivity 

was associated with high lifetime number of sex partners, among other risk factors (Geis et al. 

2011; Wand and Ramjee 2012; Mtenga et al. 2015). Although most of these studies found that 

young age of sexual debut was associated with HIV seropositivity, our results did not corroborate 

this finding. Age of sexual debut may have not have been reported correctly, either because 

respondents did not feel it socially acceptable to start sex at a young age, or because of recall bias. 

We also found that, among women, being previously married was independently associated with 

higher odds of HIV seropositivity. This finding agrees with other studies showing that HIV 

seropositivity is high among couples and previously married men and women in Malawi and other 

sub-Saharan countries (Grant and Soler-Hampejsek 2014; Muula 2010; Tenkorang 2014; Hong, 

Mishra, and Govindasamy 2008). It is likely that this is a selected subgroup of people whose 

spouses who may have died of AIDS or whose marriages dissolved because of risky behaviors or 

AIDS-related discrimination. Often, married couples have unprotected sex, which in itself is a 

high-risk behavior whether in marriage or outside. These two factors—lifetime number of sex 

partners and previous marriage—indicate the importance of emphasis on behavioral change 

interventions. 

Among both women and men, living close to a facility with ART services was not associated with 

higher HIV seropositivity. In Malawi women access healthcare services more than men do. In 

addition to the many health care programs that target women, women are caretakers of children. 

Therefore, they naturally have more contact with health facilities and may use HIV care services 

more frequently and earlier than men. Therefore we expected to observe an association of access 

to ART services at least among women. Our measurements of service access is a proxy based on 
18 

the facility survey conducted three years later. Service provision at the time of the DHS or before 

may not be well reflected. 

An interesting finding was the association of higher HIV seropositivity among men with 

comprehensive knowledge about HIV/AIDS, an association that has been reported before in 

Ethiopia (Hong, Mishra, and Govindasamy 2008). We think this could be due to previous 

knowledge about HIV provided through previous HIV Counselling and Testing sessions. Other 

factors significantly associated with seropositivity were urban residence and living in the Southern 

Region of Malawi. The Southern Region is the most populated and probably most urban region of 

the country. Urbanization is associated with better access to healthcare services and therefore may 

also be associated with better HIV care and survival. Also noted to be associated with 

seropositivity was the wealth index. Taken together, urban residence, living in the Southern 

Region, and high wealth status may be associated with better access to care and survival. The 

dataset was not designed to explore these factors, but our findings suggest that further exploration 

is warranted in subsequent DHS data and in prospective studies.

The strength of our results is that the data are from nationally representative data set, and we 

undertook a rigorous exploration of the dataset to include only variables that had large sample size 

and adequate variation. We also thought carefully about the biological plausibility of explanatory 

variables included in our mathematical models. Variables that did not have biological plausibility 

were included in the baseline table to demonstrate representativeness of the data but were excluded 

from our mathematical models. Our results are therefore reliable and valid within the context of 

the study. However, the results have critical limitations and therefore caution needs to be exercised 

when interpreting these results. 

We could not extensively explore explanatory factors for HIV prevalence due to limitations in the 

dataset: lack of sufficient data at the individual level. Sometimes the data structure made analysis 

difficult. For example, partners’ characteristics that have been shown to be important by other 

authors (Harling and Bärnighausen 2016) could not be modelled in our analysis because some data 

were not available in the DHS database Since HIV prevalence is a factor of both incidence and 

survival time, we planned to assess factors associated with HIV prevalence from these two aspects. 

While the DHS data were adequate on individual behavioral measures, data on access to ART 

services was at the cluster level, collected from the 2013-2014 Malawi Service Provision 

Assessment (SPA), which was conducted three years after DHS data collection was completed. 

Because of these limitations, our results should be interpreted with caution. 

Despite these limitations, our results corroborate findings from other authors demonstrating that 

risky behavior is the most important factor associated with prevalence of HIV. Notwithstanding 

the fact that our analysis is based on cross-sectional data, risky sexual behavior contributes to HIV 
19 

prevalence through incidence. Our finding that prevalence of HIV is associated with lifetime 

number of sex partners is similar to findings by other further analysis of DHS data and cohort 

studies (Kenyon, Tsoumanis, and Schwartz 2015; Akwara et al. 2005; Geis et al. 2011; Wand and 

Ramjee 2012; Mtenga et al. 2015). 

We also found that, among women, being previously married was independently associated with 

higher odds of being HIV-positive. This finding agrees with other studies in Malawi and other subSaharan countries showing that HIV seropositivity is high among couples and previously married 

men and women (Grant and Soler-Hampejsek 2014; Muula 2010; Tenkorang 2014; Hong, Mishra, 

and Govindasamy 2008). As mentioned in the Results section, it is likely that this is a selected 

subgroup whose spouses who may have died of HIV or whose marriages dissolved because of 

risky behaviors or AIDS-related discrimination. 

21 

5. Conclusion 

Our analysis suggests that educational attainment is not associated with HIV prevalence after 

controlling for a number of sociodemographic characteristics and behavioral factors. Unlike the 

case with other diseases, higher levels of education do not appear to be protective against HIV 

seropositivity. Given the evidence that higher socioeconomic status improves access to health care 

in general, and to ARTs in particular, interventions might focus on reducing risky behaviors in this 

target population. Considering that educational institutions offer opportunities of easier targeting, 

HIV-prevention health promotion activities need to be supported and highly emphasized among 

students. 

23 

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