Discriminatory attitude towards people living with HIV/AIDS and its associated factors among adult population in 15 sub-Saharan African nations.

BACKGROUND: Discrimination of people living with HIV/AIDS is one of the reported obstacles to the achievement of universal access to HIV/AIDS prevention, treatment, care, and support programs. Many international agencies have made combating HIV/AIDS stigma and discrimination a top priority. However, previous evidence in different parts of Africa revealed that the magnitude of HIV/AIDS-related discriminatory attitude is significantly high.
OBJECTIVE: To assess discriminatory attitude towards people living with HIV/AIDS and its associated factors among the adult population in 15 sub-Saharan African nations.
METHODS: We have used the 15 Demographic and Health Survey data that were conducted in sub-Saharan Africa (SSA) from 2015 to 2019/20. Each country's data was appended and a total weighted sample of 318,186 (unweighted sample = 315,448) adults who had ever heard of AIDS was used for the final analysis. The two discriminatory attitude questions were used to get the outcome variable and those who answered "Yes" or "don't know" for both questions were counted as if they had no discriminatory attitude towards people living with HIV/AIDS. To assess the factors associated with discriminatory attitude towards people living with HIV/AIDS, we have fitted a multilevel binary logistic regression model. Bivariable analysis was done to select eligible variables for the multivariable analysis. Finally, variables with p<0.05, in the multivariable analysis, were considered as significant predictors of discriminatory attitude towards people living with HIV/AIDS.
RESULTS: The prevalence of discriminatory attitude towards HIV/AIDS in the 15 sub-Saharan African nations was 47.08% (95% CI: 47.08, 47.42), which ranges from 17.64% (95% CI: 17.22, 18.07) in Malawi to 79.75% (95% CI: 79.02, 80.45) in Guinea. In the multivariable analysis, both individual level and community level variables were significantly associated with discriminatory attitude towards people living with HIV/AIDS. Being younger age, no formal education, never married, low socioeconomic status, male-headed household, non-contraceptive use, no mass media exposure, and incorrect comprehensive knowledge towards HIV/AIDS were among the individual-level factors that were associated with higher odds of discriminatory attitude towards people living with HIV/AIDS. While being from urban residence and the western SSA region were among the community-level factors that were significantly associated with higher odds discriminatory attitude towards people living with HIV/AIDS.
CONCLUSION: The prevalence of discriminatory attitude towards people living with HIV/AIDS in 15 sub-Saharan African nations was high. Both individual and community-level factors were associated with discriminatory attitude towards people living with HIV/AIDS. Therefore, special attention should be given to those who are poor, uneducated, and younger adults. In addition, it is better to strengthen the accessibilities of different media for adult populations to create an appropriate attitude towards people with HIV/AIDS.

Background

The burden of the global HIV epidemic is disproportionately concentrated in sub-Saharan Africa, in which in 2017 about 75% of deaths and 65% of new infections occurred and where 71% of people living with HIV exist in [1, 2]. To stop and reverse the spread of HIV/AIDS, Sustainable Development Goal 3 calls for the end of the epidemic by 2030. In addition, the Joint United Nations Programme on HIV/AIDS (UNAIDS) sets a goal of reducing both new infections and deaths by 2030. Despite these goals, a recent review of the state of HIV concluded that the world is not at the potential to end the HIV epidemic [3-5].

Discrimination, one of the reported obstacles to the achievement of universal access to HIV/AIDS prevention, treatment, care, and support programs, is a differential action or behavior towards the stigmatized person based on those attitudes and perceptions [6]. The reason behind HIV/AIDS-related discrimination is due to the nature of HIV/AIDS, which is due to its incurability, and fatality, its contagiousness and transmissibility, and the repellent, ugly, and upsetting appearance of the infected individual at the advanced stages of the disease [7]. The other reason will be its transmission modality, transmitted through sexual intercourse that is viewed as a consequence of sexual immoral behaviors; thus, people living with HIV are severely discriminated regardless of how they actually became infected [8].

Many international agencies including the World Health Organization, the Joint United Nations Programme on HIV and AIDS, and the United States Agency for International Development have made combating HIV/AIDS stigma and discrimination a top priority, as this phenomenon undermines public health efforts to combat the pandemic [9, 10]. Extensive discriminatory attitude in a population can impair people’s desire to be tested for HIV, their commencement of and adherence to antiretroviral therapy, social support, disclosing their status to family members, colleagues, and sexual partners and finally it affects their quality of life [11-16].

Previous evidence in different parts of Africa revealed that the magnitude of HIV/AIDS-related discriminatory attitude is significantly high, which ranges from 40% to 93.8% [17-19]. Studies have shown that different factors such as sex of respondent, educational status, mass media exposure, age, employment, comprehensive knowledge, place of residence, and community level of education are important factors associated with discriminatory attitude towards people living with HIV/AIDS [17, 20–28].

Due to the abovementioned negative effects of discriminatory attitude, there must be interventions to reduce discriminatory attitudes for combating HIV/AIDS transmission, as well as for increasing the quality of life of people living with HIV. Even though discrimination is a devastating issue, particularly in sub-Saharan Africa, the factors associated with discriminatory attitude towards people living with HIV/AIDS in sub-Saharan Africa is understudied. Therefore, this study aimed to assess discriminatory attitudes towards people living with HIV/AIDS and its associated factors in 15 sub-Saharan African nations. This could help policymakers and other responsible bodies combat this devastating problem at the country and regional levels.

Methods

Data source and study population

We have used DHS that were conducted from 2015 to 2019/20, conducted in the last five years. Even though, in SSA, there were 20 countries DHS that were conducted between 2015 and 2019/2020, for this study, only 15 countries DHS was used since there were not data about the outcome variable in the three country surveys (South Africa, Senegal, Chad, Tanzania, and Rwanda DHS had no observation regarding the outcome variable). First, we have appended both individual record women’s file and men’s file for each country. Finally, each countries data was appended and a total weighted sample of 318,186 (98,322 men and 219,864 women) adults, total unweighted sample of 315448 adults, who had ever heard of AIDS were used for the final analysis (Table 1).

Table 1

Percentage distribution of study participants by country and region of Africa.

Countries with their Region	Year of study	Unweighted sample size (315,186)	Unweighted Percentage (%)	Weighted sample size (318,186)	Weighted Percentage (%)
Eastern Africa		145,385	46.09	145,797	45.82
Burundi	2016/17	23,070	7.31	23,050	7.24
Ethiopia	2016	25,542	8.10	25,927	8.15
Uganda	2018/19	23,438	7.43	23,437	7.37
Zambia	2018	24,361	7.72	24,463	7.69
Zimbabwe	2015	17,831	5.65	17,845	5.61
Malawi	2017	31,143	9.87	31,075	9.77
Western Africa		134,846	42.75	136,418	42.87
Benin	2017/18	13,262	4.20	13,193	4.15
Sierra Leone	2019	20,498	6.50	20,719	6.51
Gambia	2019/20	15,627	4.95	15,740	4.95
Guinea	2018	11,957	3.79	12,112	3.82
Liberia	2019/20	11,251	3.57	11,293	3.55
Mali	2018	11,803	3.74	12,653	3.98
Nigeria	2018	50,448	15.99	50,708	15.94
Central Africa		35,217	11.16	35,970	11.30
Angola	2015/16	16,161	5.12	16,736	5.26
Cameroon	2018	19,056	6.04	19,235	6.05

Study variables

Outcome variable

Our outcome variable was the discriminatory attitude towards people living with HIV/AIDS, which is a binary outcome variable. The two discriminatory attitude questions (Would you buy vegetables from a vendor with HIV and children with HIV should be allowed to attend school with children without HIV) were used to get the outcome variable. Individuals had a discriminatory attitude towards people living with HIV/AIDS only if they answer “No” for both questions. Those who answered “Yes” or “don’t know” had counted as if they had no discriminatory attitude towards people living with HIV/AIDS [29].

Independent variables

For assessing the factors associated with discriminatory attitude towards people living with HIV/AIDS, both individual and community level independent variables were incorporated.

Individual-level variables. Sex, age, educational level, current marital status, wealth index, occupation, media exposure, sex of household head, contraceptive use, and comprehensive knowledge towards HIV/AIDS were incorporated as individual-level variables.

Community-level variables. Residence, African region, community illiteracy level, and community level of media exposure were the community-level variables.

Operational definition

Media exposure. Created by combining whether a respondent reads a newspaper, listens to the radio, and watches television and coded as yes (if an individual had been exposed to at least one of these media) and no (otherwise).

Comprehensive knowledge of HIV/AIDS. A composite score of six different questions: 1. We can get HIV by witchcraft or supernatural means (yes/no), 2. Consistent use of condoms during sexual intercourse can reduce the chance of getting HIV (yes/no), 3. Having just one uninfected faithful partner can reduce the chance of getting HIV (yes/no), 4. Can get HIV from mosquito bites (Yes/no), 5. Can get HIV by sharing food with a person who has HIV/AIDS (Yes/no), and 6. A healthy-looking person can have HIV (Yes/no). Then the respondent had correct comprehensive knowledge if she/he answers all the six questions correctly and not knowledgeable if she/he did not give the correct answer for at least one of the questions.

Community illiteracy level. It was the proportion of adults with no formal education derived from data on respondent’s level of education. Then, it was categorized using national median value to values: low (if the individual was from communities in which ≤50% of adults had no formal education) and high (if the individual was from communities in which >50% of adults had no formal education) community illiteracy level.

Community-level of media exposure. The proportions of adults who were exposed to media within a specific cluster. It was categorized in the same fashion as the community illiteracy level into low and high community-level of media exposure.

Data management and statistical analysis

We have used Stata version 14 (StataCorp. 2015. Stata: Release 14. Statistical Software. College Station, TX: StataCorp LLC) for appending, extracting, and analyzing data. Throughout the statistical analysis, we have weighted the data to restore the representativeness of the sample and to get a robust standard error (an appropriate statistical estimate). The results of descriptive analyses were reported using texts, tables, and graphs.

To assess the factors that were associated with discriminatory attitude towards people living with HIV/AIDS, we have used a multilevel logistic regression analysis since the DHS data had hierarchical nature and the outcome variable was binary. We have fitted four models (Model 1, 2, 3, and 4) while conducting multilevel analysis. The model I was fitted with only the outcome variable to assess the variability of discriminatory attitude towards people living with HIV/AIDS between clusters; Model 2 fitted with only individual-level variables; Model 3 and Model 4 was fitted with community level variables only and both individual and community level variables respectively.

To determine the community level variability of discriminatory attitude towards people living with HIV/AIDS, we have conducted a random-effects analysis. In the random effect analysis we have calculated the Intraclass Correlation Coefficient (ICC); to indicate the amount of variation of having a discriminatory attitude towards people living with HIV/AIDS that could be due to variability between cluster/communities and Proportional Change in Variance (PCV); to show to what extent discriminatory attitude towards people living with HIV/AIDS was explained by the fitted model. Of the fitted four models, the best-fitted model was selected using deviance (a model with the lowest deviance, model 4, was the best-fitted model).

For selecting eligible variables for the multivariable analysis, we have done the bivariable analysis. Those variables with a p<0.20, in the bivariable analysis, were eligible for the multivariable analysis and, in the multivariable analysis; the adjusted odds ratio (AOR) with its 95% confidence interval (CI) was reported. Finally, variables with p≤0.05 were considered as significant predictors of discriminatory attitude towards people living with HIV/AIDS.

Ethical consideration

Since we were using publicly accessible data, ethical approval was not needed. In addition, this research was considered exempt by the Institute of Public Health, College of Medicine and Health Sciences, University of Gondar Institutional Review Committee. However, by registering or online requesting we have accessed the data set from the DHS website (https://dhsprogram.com).

Results

Sociodemographic characteristics of respondents

Total weighted samples of 318,186 individuals were used for the final analysis. More than two-thirds (69.10%) of participants were females. The majority (60.16%) of respondents were married during the survey and around 24.92% of the respondents were from the richest households. Around three-fourth (76.96%) of the study participants were from male-headed households. A majority (70.81%) of respondents were exposed to at least one media (radio, television, or newspaper) and only 41.39% of respondents had comprehensive knowledge regarding HIV/AIDS (Table 2).

Table 2

Sociodemographic characteristics of respondents.

Variables	Weighted frequency (n = 318,186)	Percentage (%)
Sex
Male	98322	30.90
Female	219864	69.10
Age
15–19	69750	21.92
20–24	57407	18.04
25–29	53737	16.89
30–34	44922	14.12
35–39	39120	12.29
40–44	29503	9.27
45–49	23747	7.46
Educational status
No education	80579	25.32
Primary	98201	30.86
Secondary	116107	36.49
Higher	23299	7.32
Occupation
Not working	103577	32.55
Working	214609	67.45
Marital status
Single	104807	32.94
Married	191411	60.16
Widowed/separated/divorced	21968	6.90
Wealth status
Poorest	52009	16.35
Poorer	56870	17.87
Middle	61092	19.20
Richer	68909	21.66
Richest	79306	24.92
Sex of household head
Male	232766	76.96
Female	69680	23.04
Contraceptive use
No	232676	73.13
Yes	85510	26.87
Media exposure
No	92875	29.19
Yes	225311	70.81
Comprehensive knowledge of HIV/AIDS
No	186498	58.61
Yes	131688	41.39
Residence
Urban	130125	40.90
Rural	188061	59.10
Community-level of women literacy
Low	165368	51.97
High	152818	48.03
Community-level media exposure
Low	154531	48.57
High	163655	51.43

Prevalence of discriminatory attitude towards people living with HIV/AIDS in 15 sub-Saharan African nations

The prevalence of discriminatory attitude towards people living with HIV/AIDS in SSA was 47.08% (95% CI: 47.08, 47.42), with huge variation between countries that ranges from 17.64% (95% CI: 17.22, 18.07) in Malawi to 79.75% (95% CI: 79.02, 80.45) in Guinea (Fig 1).

Fig 1

Prevalence of discriminatory attitude towards people living with HIV/AIDS by countries in sub-Saharan Africa.

Prevalence by African region, particularly by sub-Saharan African region

The prevalence of discriminatory attitude towards people living with HIV/AIDS was highest, 68.20 (95%CI: 67.95, 68.45), in the western African region (Fig 2).

Fig 2

Prevalence of discriminatory attitude towards people living with HIV/AIDS by sub-Saharan Africa regions.

Factors associated with discriminatory attitudes towards people living with HIV/AIDS

Fixed effect analysis

Both bivariable and multivariable analysis was conducted. In this study, all independent variables had p<0.20 in the bivariable analysis. Therefore, all variables were eligible for the multivariable analysis. In the multivariable analysis, both individual level and community level variables were associated with discriminatory attitude towards people living with HIV/AIDS. Age of the respondent, educational status, marital status, wealth index, sex of household head, contraceptive use, mass media exposure, and comprehensive knowledge towards HIV/AIDS were among the individual-level factors that were associated with discriminatory attitude towards people living with HIV/AIDS. While residence and SSA region were among the community-level factors that were significantly associated with discriminatory attitude towards people living with HIV/AIDS (Table 3).

Table 3

Multilevel analysis for assessing factors associated with discriminatory attitude towards people living with HIV/AIDS in sub-Saharan Africa.

Variables	Model 1	Model 2	Model 3	Model 4
Sex
Male		1.00		1.00
Female		1.05 (1.02, 1.088		1.02 (0.99, 1.06)
Age
15–19		1.00		1.00
20–24		0.81 (0.78, 0.84)		0.80 (0.78, 0.83)
25–29		0.77 (0.74, 0.80)		0.76 (0.73, 0.79)
30–34		0.68 (0.65, 0.70)		0.67 (0.64, 0.70)
35–39		0.69 (0.65, 0.71)		0.67 (0.64, 0.70)
40–44		0.63 (0.60, 0.66)		0.63 (0.60, 0.66)
45–49		0.66 (0.62, 0.69)		0.65 (0.62, 0.68)
Educational status
No education		1.00		1.00
Primary		0.40 (0.39, 0.42)		0.44 (0.42, 0.46)
Secondary		0.36 (0.35, 0.38)		0.37 (0.36, 0.39)
Higher		0.27 (0.26, 0.29)		0.27 (0.26, 0.29)
Occupation
Not working		1.00		1.00
Working		1.02(0.99, 1.05)		1.02 (0.99, 1.05)
Marital status
Single		1.00		1.00
Married		0.94 (0.90, 0.97)		0.93 (0.90, 0.96)
Widowed/separated/divorced		0.69(0.66, 0.73)		0.70 (0.66, 0.74)
Wealth status
Poorest		1.00		1.00
Poorer		0.92 (0.88, 0.96)		0.90 (0.87, 0.95)
Middle		0.83 (0.79, 0.87)		0.78 (0.74, 0.82)
Richer		0.75 (0.71, 0.80)		0.65 (0.61, 0.69)
Richest		0.63 (0.58, 0.67)		0.49 (0.46, 0.53)
Sex of household head
Male		1.00		1.00
Female		0.89 (0.86, 0.92)		0.90 (0.87–0.92)
Contraceptive use
No		1.00		1.00
Yes		0.72 (0.70, 0.74)		0.74 (0.72, 0.76)
Media exposure
No		1.00		1.00
Yes		1.01 (0.98, 1.04)		0.95 (0.92, 0.98)
Comprehensive knowledge of HIV/AIDS
No		1.00		1.00
Yes		0.38 (0.37, 0.39)		0.39 (0.38, 0.40)
Residence
Urban			1.00	1.00
Rural			1.30 (1.21, 1.39)	0.67 (0.63, 0.72)
Region of Africa
West Africa			1.00	1.00
Central Africa			0.50 (0.46, 0.55)	0.53 (0.50, 0.56)
East Africa			0.49 (0.46, 0.52)	0.64 (0.61, 0.68)
Community-level of women literacy
Low			1.00	1.00
High			1.01(0.85, 1.19)	0.84 (0.75, 1.01)
Community-level media exposure
Low			1.00	1.00
High			0.99 (0.84, 1.16)	1.06 (0.91, 1.24)

The odds of having discriminatory attitude towards people living with HIV/AIDS among participants in the age group 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years were 20% (AOR = 0.80; 95%CI: 0.78, 0.83), 24% (AOR = 0.76; 95%CI: 0.73, 0.79), 33% (AOR = 0.67; 95%CI: 0.64, 0.70), 33% (AOR = 0.67; 95%CI: 0.64, 0.70), 37% (AOR = 0.63; 95%CI: 0.60, 0.66), and 35% (AOR = 0.65; 95%CI: 0.62, 0.68) lower as compared to those participants who were in the age group 15–19 years respectively. Being having primary, secondary, and higher education had 56% (AOR = 0.44; 95%CI: 0.42, 0.46), 63% (AOR = 0.37; 95%CI: 0.36, 0.39), and 73% (AOR = 0.27; 95%CI: 0.26, 0.29) lower odds of discriminatory attitude towards people living with HIV/AIDS respectively, as compared to those who had no formal education. The odds of having a discriminatory attitude towards people living with HIV/AIDS among those who were married and divorced/separated/widowed were 7% (AOR = 0.93; 95%CI: 0.90, 0.96), and 30% (AOR = 0.70; 95%CI: 0.66, 0.74) lower respectively, as compared to those who were never married. Regarding wealth status, those individuals who were from poorer, middle, rich, and richest households had lower odds of having a discriminatory attitude towards people living with HIV/AIDS as compared to those who were from the poorest households. The odds of having a discriminatory attitude towards people living with HIV/AIDS among those individuals who were from female-headed households were 10% (AOR = 0.90; 95%CI: 0.87, 0.92) lower as compared to their counterparts. Being using contraceptive methods was associated with 26% (AOR = 0.74; 95%CI: 0.72, 0.76) lower odds of discriminatory attitude towards people living with HIV/AIDS as compared to their counterparts. Being having mass media exposure was associated with 5% (AOR = 0.95; 95%CI: 0.92, 0.98) lower odds of having a discriminatory attitude towards people living with HIV/AIDS as compared to those who had no mass media exposure. The odds of having a discriminatory attitude towards people living with HIV/AIDS were 61% (AOR = 0.39; 95%CI: 0.38, 0.40) lower among those individuals who had comprehensive knowledge towards HIV/AIDS as compared to those who had no comprehensive knowledge towards HIV/AIDS. Those individuals who were from the rural area had 33% (AOR = 0.67; 95%CI: 0.63, 0.72) lower odds of discriminatory attitude towards people living with HIV/AIDS as compared to their counterparts. Moreover, being from the Central and Eastern SSA region were 47% (AOR = 0.53; 95%CI: 0.50, 0.56) and 36% (AOR = 0.64; 95%CI: 0.61, 0.68) lower odds of having a discriminatory attitude towards people living with HIV/AIDS, respectively, as compared to those from Western SSA region (Table 3).

Random effect analysis

As shown in Table 4, the ICC value in model 1 indicates that about 21.1% of the variability of discriminatory attitude towards people living with HIV/AIDS was attributable due to the difference between communities/clusters. Besides, the highest PCV value in model 3 revealed that 48.32% of the variability of discriminatory attitude towards people living with HIV/AIDS was explained by both individual and community-level variables. Moreover, the lowest deviance, which was 382,838.48, in model 4 revealed that model 4 was the best-fitted model for the data (Table 4).

Table 4

Community-level variability (random effect analysis) of discriminatory attitude towards people living with HIV/AIDS and model fitness.

Parameter	Model 1	Model 2	Model 3	Model 4
Community variability (SE)	0.880 (0.050)	0.515 (0.035)	0.647 (0.046)	0.446 (0.033)
ICC (%)	21.10	13.53	16.43	11.94
PCV (%)	Reference	41.48	26.48	49.32
Model fitness
Deviance	429,503.06	387,376.12	420,875.86	382,838.48

Note: SE = Standard Error, ICC = Intraclass Correlation Coefficient, PCV = Proportionate Change in Variance.

Discussion

This study aimed to assess discriminatory attitude towards people living with HIV/AIDS and its associated factors in 15 sub-Saharan African nations. In this study, the prevalence of discriminatory attitude towards people living with HIV/AIDS was 47.08% (95% CI: 47.08, 47.42). This finding is lower than a study conducted in Ethiopia and Nigeria [17, 18] and higher than a study finding in Pakistan [23]. This discrepancy may be due to the difference in the study population (since this study incorporates study participants in SSA), sociocultural and socioeconomic differences between countries, as well as due to the difference in the study period and sample size (this study was based on pooled analysis).

This study also identified the individual and community level variables that were associated with discriminatory attitude towards people living with HIV/AIDS. The odds of having a discriminatory attitude towards people living with HIV/AIDS was higher among younger adults. This is congruent with studies conducted in Botswana [19]. This could be because people of a younger age are more reliant on their families and are less likely to get HIV/AIDS information. Furthermore, this group of people may not have the opportunity to get health education at the workplace or through other experiences that older people may have. Being not having formal education was associated with higher odds of discriminatory attitude towards people living with HIV/AIDS. This finding coincides with other study findings in Ethiopia [17, 28], Pakistan [23, 27], and Tajikistan [26]. The possible explanation is being educated enhances knowledge of a person in general and their exposure to modern media and modern health facilities. Besides, it may be since education is a powerful tool that affects the attitudes of individuals by promoting a better understanding of HIV/AIDS. Furthermore, educated individuals are more generous in accepting people living with HIV/AIDS and in showing readiness to respect the survival and interactional rights of the victims [30]. Moreover, education accelerates favorable socio-cultural change and helps individuals challenge misconceptions and traditional beliefs associated with the epidemic and people living with HIV/AIDS [31].

Household wealth index was another factor that was associated with discriminatory attitude towards people living with HIV/AIDS. The odds of having a discriminatory attitude towards people living with HIV/AIDS was higher among those adults who were from households with low socioeconomic/wealth status. This is in agreement with studies conducted elsewhere [23, 26, 27]. This could be because people from a higher socioeconomic background had better and more relevant knowledge, maybe more educated, may have better access to media, and are more protective and conscious of their health problems. In addition, in this study, being married or having a history of marriage had lower odds of having a discriminatory attitude towards people living with HIV/AIDS as compared to those who were never married/single. This is contrary to studies conducted in Ethiopia and Nigeria, which revealed that married individuals have a higher discriminatory attitude toward people living with HIV/AIDS than singles [18, 21]. The possible reason why married/those having marriage history had a higher discriminatory attitude towards people living with HIV/AIDS, in this study, may be due to gaining knowledge through discussion with their partner or since this group of individuals might be older and have a chance of gaining knowledge regarding HIV/AIDS through their experience.

The study at hand also revealed that exposure to media was associated with lower odds of discriminatory attitude towards people living with HIV/AIDS. This is in agreement with studies conducted in Ethiopia [17, 28] and Pakistan [27]. This could be because successful media communication raises an individual’s knowledge of HIV/AIDS. In addition, the media disseminates accurate information that dismisses pre-existing myths and harmful attitudes towards HIV/AIDS. Besides, media services may assist individuals in learning more from others’ experiences and improving their perceptions of the condition and people affected by HIV/AIDS. Furthermore, the media can affect people’s attitudes and behavior regarding HIV/AIDS through boosting awareness and fostering positive attitudes, as well as fostering value systems that favor kindness and care for HIV/AIDS victims.

In addition, being having comprehensive knowledge of HIV/AIDS was associated with lower odds of discriminatory attitude towards people living with HIV/AIDS. This is in concordance with different studies conducted elsewhere [17, 22–26]. The most obvious explanation is that having accurate information regarding HIV transmission methods and the myths connected with AIDS transmission reduces stigmatizing behaviors and discriminatory attitudes toward HIV-positive people. Moreover, the current study also revealed that being from female head households and being using any contraceptive method was associated with lower odds of discriminatory attitude towards people living with HIV/AIDS.

The study at hand also revealed that community-level factors such as residence and SSA region were associated with discriminatory attitude towards people living with HIV/AIDS. Consistent with studies conducted elsewhere [21, 23, 28]; this study also identified regional variations in terms of discriminatory attitude towards people living with HIV/AIDS. This may be due to the different traditional behaviors and conservative outlooks that aggravate discrimination in regions of SSA Africa. Surprisingly and contrary to different study findings [21, 27], the current study revealed that being living in a rural area was associated with lower odds of discriminatory attitude towards people living with HIV/AIDS as compared to those who were from urban areas. This may be due to the advancement of the extension program in remote and rural areas. However, the authors recommend further investigation in this regard.

This study was based on representative data from SSA and with suitable statistical analysis (multilevel analysis). As a result, policymakers, as well as governmental and non-governmental groups, can use it to make relevant actions. The study, however, had limitations because it was based on the information provided in the survey data and, therefore, we cannot incorporate the important variables such as cultural norms and perceptions about people living with HIV/AIDS. Besides, existing data sets only allows use of variables measured in the existing data set, as it is a case for the outcome variable that is measured by only two questions. Furthermore, because it was based on survey data, the cause and effect relationship between the outcome variable and independent variables cannot be demonstrated. As a result, caution is advised when interpreting the study’s findings.

Conclusion

The prevalence of discriminatory attitude towards people living with HIV/AIDS in SSA was high. Both individual and community-level factors were associated with discriminatory attitude towards people living with HIV/AIDS. Therefore, special attention should be given to those who are poor, uneducated, and younger adults. In addition, it is better to strengthen the accessibilities of different media for adult populations to create an appropriate attitude towards people with HIV/AIDS.

Sociodemographic characteristics of respondents by regions of Africa.

(DOCX)

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Multilevel analysis for assessing factors associated with discriminatory attitude towards people living with HIV/AIDS among reproductive age women and men, analyzed separately.

(DOCX)

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Multilevel analysis for assessing factors associated with discriminatory attitude towards people living with HIV/AIDS by regions of sub-Saharan Africa.

(DOCX)

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4 Sep 2021

PONE-D-21-17959

Discriminatory attitude towards people living with HIV/AIDS and its associated factors among adult population in Sub-Saharan Africa.

PLOS ONE

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Additional Editor Comments (if provided):

This is an important contribution to raise the awareness of discriminatory attitudes towards people living with HIV/AIDS. The authors used DHS datasets from 15 countries and conducted two analyses. One for the prevalence of discrimination. And to study the factors they used multilevel regression analysis to identify potential determinants. However, a few issues.

I. Major

1. DHS is designed to collect mother and child information. The HIV information is either secondary or additional on these surveys. This may make the woman selected to be likely to have had a recent pregnancy (or married) when compared to another woman in the community. How this was accounted for here? And why not do an additional analysis with males and females separated?

2. Moreover to the previous point, this analysis disregards that the SSA is not one big Africa. Another analysis that looks at least regionally would be appropriate here.

3. Discrimination is a bit hard to measure. The two questions used to assess this may not perform the same way across the countries included. This must be discussed.

4. It is to cause concern that the Southern Africa region is not included in the analysis when there are countries for that region (Malawi, Zimbabwe and Zambia). What classification did the authors use?

5. Statistical analysis. Why use deviance to decide which model to choose? The deviance does not penalize the addition of more variables. BIC or AIC would be better. Anyways, I would keep model 4 by pre-specification (and the analysis has been done).

II. Minor:

Abstract

1. Please add the unweighted numbers to the results.

Background

No comments

Methods

1. Line 97 - please list the countries included together with the name of the region

2. Line 100 - please add the unweighted numbers

3. Lines 120 to 123 are a repetition of what is stated between lines 136 to 146.

4. Lines 145 please cite Stata properly

5. For the prevalence calculation how the confidence intervals were computed

6. Did you incorporate the weights in the multilevel models?

Results

1. Table 1 please add the unweighted counts.

2. Table 2 please do this table per country (or region) and add such a table to the supplements.

3. Table 3 - in the supplements add one table for the females and another for males. As well there should by region.

4. Figure 2 - There is some strange grouping of the regions. Why not the Southern region?

5. Lines 199 - The fixed effects analysis is not documented in the statistical analysis. Can you describe what is done in this model?

Discussion

1. Line 320 - what “author”? Shouldn’t be “authors”?

2. Please expand the discussion of the limitations in light of the issues raised above

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Suggest title revision: Discriminatory attitude .... in 15 sub-Saharan African nations (there are a total of some 50)

Add section on limitations, i.e. existing data sets only allows use of variable measured in the existing data set, particularly a potential issue with the dichotomous dependent variable.

Expand implications for HIV work, i.e. targeting identify sub-groups - conclusion is very brief.

Reviewer #2: Thank you for your efforts in writing this manuscript.

Generally, HIV-related discrimination remains high but varies among countries. As of 2019 Guinea had about 80% and South Africa about 16.9% of people with discriminatory attitudes towards people living with HIV/AIDS. Is there a reason why South Africa, with the highest number of people living with HIV/AIDS is not included in your study despite the availability of data on South Africa from the Population-based Survey 2014-2018? What has South Africans done differently to reduce discriminatory attitudes as compared to Guinea and other countries in your study? Data from South Africa is pertinent and needs to be discussed in your manuscript.

Please check the following lines:

87 peoples?

99 (why women in brackets) Why?

299 peoples?

325 (residual confounders is there) Expain what you mean here.

Page 36: Prevalence...by individual countries

It should read country and not 'conutry'

Proofreading is necessary.

**********

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Reviewer #1: Yes: William L Holzemer

Reviewer #2: No

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19 Oct 2021

October18, 2021

Authors’ response to editor’s and reviewers comments

Title: Discriminatory attitude towards people living with HIV/AIDS and its associated factors among adult population in Sub-Saharan Africa.

Manuscript number: PONE-D-21-17959

Dear all thank you for your constructive comments for the betterment of our manuscript. Below is the point-by-point response for issues you raised. In addition, we have amended our manuscript based on your comments, suggestions, and journals guideline.

Response to Editor comments

1. DHS is designed to collect mother and child information. The HIV information is either secondary or additional on these surveys. This may make the woman selected to be likely to have had a recent pregnancy (or married) when compared to another woman in the community. How this was accounted for here? And why not do an additional analysis with males and females separated?

Author’s response: Thank you for the comment. In the DHS survey all women age 15-49 and all men age 15-59 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed.

Our main intention here, in this analysis, is to assess discriminatory attitude and its associated factors among adult populations. There are many individual studies that are conducted to this problem among males and females separately. Therefore, we have conducted this study whether the problem (discriminatory attitude) is different between males and females using sex as one of the factor.

In the DHS data there is HIV information. However, due to the secondary nature of the data, it may not contain all information and this is acknowledged in the discussion section.

2. Moreover to the previous point, this analysis disregards that the SSA is not one big Africa. Another analysis that looks at least regionally would be appropriate here.

Author’s response: Thank you. Based on your recommendation, we have conducted analysis regionally and put this analysis as supplementary file (see S3 Table).

3. Discrimination is a bit hard to measure. The two questions used to assess this may not perform the same way across the countries included. This must be discussed.

Author’s response: Dear Editor, Thank you for raising an important issue. In the DHS survey, the two questions were used to assess the discriminatory attitude towards HIV/AIDS and these were collected in similar way in all DHS surveys, except in some country surveys in which there is no collected information about discriminatory attitude (we have excluded such surveys in this study). However, measuring discriminatory attitude using these two questions is not enough and this is acknowledged, in the revised manuscript, as limitation in the discussion section.

4. It is to cause concern that the Southern Africa region is not included in the analysis when there are countries for that region (Malawi, Zimbabwe and Zambia). What classification did the authors use?

Author’s response: Dear Editor, thank you. There are four African regions namely, Eastern, western, southern, and central. However, for this study, there was no countries in southern region with full information about discriminatory attitude. Therefore, we have assessed discriminatory attitude in three regions of Africa, particularly regions in sub-Saharan Africa. The above-mentioned countries (Malawi, Zimbabwe and Zambia) are under eastern Africa. As you know there are different classifications of African regions, however, many studies such as Tessema ZT et al, 2020 also considers these countries as Eastern Africa. In addition, when we access the DHS data these countries are under East African countries.

5. Statistical analysis. Why use deviance to decide which model to choose? The deviance does not penalize the addition of more variables. BIC or AIC would be better. Anyways, I would keep model 4 by pre-specification (and the analysis has been done).

Author’s response: Thank you. We have used Deviance to choose our model since the models were nested.

6. Abstract

Please add the unweighted numbers to the results.

Author’s response: Thank you. We have added the unweighted numbers to the results

7. Methods

1. Line 97 - please list the countries included together with the name of the region

Author’s response: Thank you for the important comment you raised. However, this statement is re arranged and we only put list of countries we have used for the final analysis with their respective region in the revised manuscript (see Table 1).

2. Line 100 - please add the unweighted numbers

Author’s response: Thank you. We have added the unweighted numbers.

3. Lines 120 to 123 are a repetition of what is stated between lines 136 to 146.

Author’s response: We have considered your comment and removed the statements/phrases presented in line 120 to 123 in the revised manuscript.

4. Lines 145 please cite Stata properly

Author’s response: Thank you. We have considered the comment in the revised manuscript.

5. For the prevalence calculation how the confidence intervals were computed

Author’s response: We have calculated the confidence interval for the prevalence using “prop outcome variable” stata command.

6. Did you incorporate the weights in the multilevel models?

Author’s response: Yes, the multilevel models was all weighted.

8. Results

1. Table 1 please add the unweighted counts.

Author’s response: Thank you. Based on your comment, we have added the unweighted counts in Table 1.

2. Table 2 please do this table per country (or region) and add such a table to the supplements.

Author’s response: We have considered your comment in the revised manuscript (see S1 Table).

3. Table 3 - in the supplements add one table for the females and another for males. As well there should by region.

Author’s response: Thank you. We have incorporated the multilevel analysis result for males and females, as well as per region as supplementary file (See S2 Table and S3 Table).

4. Figure 2 - There is some strange grouping of the regions. Why not the Southern region?

Author’s response: Thank you. There was no countries with recorded information about discriminatory attitude in countries from southern African region and that is why we did not incorporate southern African region in the whole analysis.

5. Lines 199 - The fixed effects analysis is not documented in the statistical analysis. Can you describe what is done in this model?

Author’s response: Thank you for your comment. The fixed effect analysis means simply the analysis conducted to assess factors associated with discriminatory attitude. For your information, table 3 and its description in the fixed effects analysis section is in general the fixed effect analysis result.

9. Discussion

1. Line 320 - what “author”? Shouldn’t be “authors”?

Author’s response: Considered in the revised manuscript.

2. Please expand the discussion of the limitations in light of the issues raised above

Author’s response: Thank you we have revised the discussion of the limitation based on your comment and reviewers suggestion.

Response to Reviewer #1 comments:

1. Suggest title revision: Discriminatory attitude .... in 15 sub-Saharan African nations (there are a total of some 50)

Author’s response: Thank you for your comment. We have adjust our title according to your comment.

Add section on limitations, i.e. existing data sets only allows use of variable measured in the existing data set, particularly a potential issue with the dichotomous dependent variable.

Expand implications for HIV work, i.e. targeting identify sub-groups - conclusion is very brief.

Author’s response: Thank you for the important issue raised. We have incorporated your suggestion in the revised manuscript.

Response to Reviewer #2 comments:

1. Generally, HIV-related discrimination remains high but varies among countries. As of 2019 Guinea had about 80% and South Africa about 16.9% of people with discriminatory attitudes towards people living with HIV/AIDS. Is there a reason why South Africa, with the highest number of people living with HIV/AIDS is not included in your study despite the availability of data on South Africa from the Population-based Survey 2014-2018? What has South Africans done differently to reduce discriminatory attitudes as compared to Guinea and other countries in your study? Data from South Africa is pertinent and needs to be discussed in your manuscript.

Author’s response: Thank you for the important comment. Even though south Africa had the most recent DHS survey conducted between 2015 to 2020, it had no recorded information about discriminatory attitude (i.e variables v825 and v857a had no observation).

2. Please check the following lines:

87 peoples?

99 (why women in brackets) Why?

299 peoples?

325 (residual confounders is there) Expain what you mean here.

Page 36: Prevalence...by individual countries

It should read country and not 'conutry'

Proofreading is necessary.

Author’s response: Thank you. We have considered these comments in the revised manuscript.

Submitted filename: Authors response #1.docx

Click here for additional data file.

15 Dec 2021

Discriminatory attitude towards people living with HIV/AIDS and its associated factors among adult population in 15 sub-Saharan African nations.

PONE-D-21-17959R1

Dear Dr. Teshale,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

This is the second revision of this manuscript The authors responded fully to the reviewers' comments.

Few more issues:

1. Please correct STATA to Stata. Stata is not an acronym (see the official Stata documentation).

2. I am OK with (or not) the use of weighted multilevel analysis. In my response, the authors said that they did use weighted multilevel analysis. I would ask them to make this clear in the text as well. The use of weights here could be a bit problematic as the weights from each survey could be standardized (or not) and therefore lead to questions like whether these weights are comparable. So, some may see issues. Could you add the unweighted multilevel analysis in the supplementary materials as well? (for the same models in table 3)

3. Please make sure that fractional numbers (in proportions/fractions) have 1 or 2 (choose one) decimal places and stick to this. It is not OK that in the very same paragraph (lines 175 to 181) some have 2 decimals and other (the 41%) none decimal places.

4. Line 177 - better say something like “better of” rather than “richest households”.

5. Table 2 - please state that the frequencies are weighted.

6. How the prevalence for the regions was computed (figure 2)? Would be good to use a meta-synthesis for this rather than simple weighted prevalence.

7. Tables in the supplementary materials have **** or something similar, can you put below the table what those mean?

8. Line 247 the 382838.4 corresponds to model 4 not to model 3.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Thank you for considering some of my comments in revising your manuscript. However, your manuscript still needs to be proofread. There are some small errors that should be looked at. For example: lines 95-98; lines 99 and more in the text.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

28 Jan 2022

PONE-D-21-17959R1

Discriminatory attitude towards people living with HIV/AIDS and its associated factors among adult population in 15 sub-Saharan African nations.

Dear Dr. Teshale:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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