Obesity and adherence to recommended fruits and vegetable intake among the HIV-positive population in Zambia

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This study assessed the prevalence of obesity and intake of fruits and vegetables to explain the problem within the Zambian context. Participants above 18 years old from health facilities with a minimum of 300 registered HIV patients were recruited and interviewed. Stata 18 SE (Stata Corp, College Station, TX, USA) was used for analysis. Participants’ characteristics were summarized using frequency and proportion for categorical variables while continuous variables were firstly checked for normality, if normally distributed then means and standard deviation were reported. The prevalence and corresponding 95% confidence intervals of risk factors were estimated. Over one tenth (10.8%) of the participants were obese. Women had a higher prevalence of overweight (26.4%) compared to men (15.2%) and more females (13.9%) were obese than males (4.5%). BMI was lower in rural (23.6 kg/m 2 ) than in urban areas (24.2 kg/m 2 ). Overweight was slightly lower in rural (21.3%) than urban communities (23.2%). Similarly, obesity was higher in urban (12.4%) than in rural areas (9.2%). The majority (74.8%) of respondents were not consuming enough fruits and vegetables with little difference between males (76.9%) and females (73.8%). The limited fruit and vegetable consumption and high salt intake indicates a dietary challenge in the management of obese HIV positive patients. It is recommended that health promotion should be incorporated in routine screening for overweight and obese individuals receiving ART. Individuals found with BMI above normal should be advised on lifestyle changes in relation to medication and diet. Nutrition & Dietetics Obesity ART Fruits Vegetables Consumption 1.0 Introduction The increasing occurrence of obesity as one of the non-communicable diseases among People living with Human Immune Virus (PLHIV) presents a challenge in management. The introduction and scaling up of access (70%) to Antiretroviral Therapy (ART) among PLHIV has led to reduction of AIDS related deaths [ 1 ]. The Zambia Population-based HIV Impact Assessment (ZAMPHIA) report shows that 89% of PLHIV know their status; 96% of these are on ART treatment; and 98% of those on ART are virally suppressed [ 2 ]. Due to improved access, PLHIV are living longer with improved quality of life [ 3 , 4 ]. Living longer is associated with new challenges. In particular, there is an increased occurrence of non-communicable diseases (NCDs) among many PLHIV [ 5 ]. A Zambian study found that age, female gender, dolutegravir (DTG) based regimen, hip circumference, T-lymphocyte count, high-sensitivity c-reactive protein and fasting blood sugar were strongly associated with metabolic syndrome among PLHIV [ 6 ]. The Zambian health system is not equipped to deal with HIV associated complications. By implication, the current focus on HIV control requires allocating significant resources to the detection, prevention, and control of NCDs especially among the PLHIV. The associated increase in non-communicable diseases such as cancers and cardiovascular diseases among HIV positive individuals is of particular concern [ 7 ]. For Zambia, morbidity and mortality associated with NCDs, among the PLHIV may contribute to reducing the achievements made towards the fight against HIV. Improved access to combination ART has led to a decline in wasting and resulted in increased numbers of overweight and obese people living with HIV [ 8 ]. Excess adiposity observed in obese individuals on ART is associated with metabolic diseases such as diabetes mellitus, liver diseases, cardiovascular disease and neurocognitive impairment [ 9 ]. Being HIV positive and carrying excess adipose tissue is implicated in systemic inflammation, arising partly due to changes in the adipose tissue and immune cell profile [ 10 , 11 ]. Increased overweight (BMI: 25.0–29.9 kg/m 2 ) and obesity (BMI ≥ 30 kg/m 2 ) has been observed among PLHIV initiated on ART [ 12 ]. The type of ART may influence weight gain. However, there is limited data to explain the gravity of obesity among PLHIV receiving ART by income or location (high-density or rural communities). For example, in vitro experiments in Houston-Texas in the USA, greater weight was reported among those, whose initial therapy was an Integrase Strand Transfer Inhibitor (INSTI) based ART regimen, in comparison to those who received Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) and Protease Inhibitor (PI) based regimens. Nonetheless, mechanisms influencing weight gain in those on ART need to be explained in context with regard to efficacy, viral suppression and reduction in metabolic disturbances and HIV infection [ 13 ]. Generally, investments in health systems strengthening and raising per-capita incomes in low-and -middle-income Countries (LMICs) are contributing to the epidemiological transition from communicable diseases in relation to ageing, lifestyle and NCDs. This is leading to a double burden, whereby communicable diseases are slowly decreasing while NCDs are rapidly increasing [ 12 , 14 , 15 ]. Meanwhile, LMIC’s health systems by design, are still focused on responding to communicable diseases with minimal investments in chronic care. Most NCDs share common traditional risk factors such as poor nutrition and physical inactivity. Considering the current limitations in the Zambian health system, the growing number of obese individuals among PLHIV is cause for concern. NCDs including obesity are expensive to manage, if not addressed, there is a possibility of diminishing the already scarce resources designated for national development; and undermine social–economic progress in the country. Although Zambia has conducted several population-based studies over the years such as the demographic and health surveys, and the Zambia Population-Based HIV Impact Assessment (ZAMPHIA), these studies have mainly focused on HIV prevalence and associated risk factors, and not on HIV and Non-Communicable Diseases. Similarly, the data from the first STEPS Survey for Zambia did not include relevant data on NCDs and associated risk factors among PLHIV. While the Electronic data system used in Zambia (SMARTCARE) collects information on various HIV-related Indicators, it remains unreliable on the quality of data collected and has incomplete information on NCDs, including high blood pressure, diabetes, dyslipidemia or obesity. Currently, there is limited nationwide data on the prevalence of non-communicable diseases such as obesity and intake of fruits and vegetables among the HIV-positive population. Understanding the burden of obesity and dietary intakes among PLHIV will help design appropriate interventions to address the growing complexity of managing co-morbidities of HIV and obesity in Zambia. The current study estimated the prevalence of obesity and adherence to the recommended dietary intake of fruits and vegetables in the diet. 2.0 Methodology 2.1 Study design A cross sectional design was followed. A stratified two-stage health facility survey was conducted between 26/10/2023 and 30/11/2023 in all ten provinces of Zambia. Initially, facilities were stratified into province and rural-urban classification into 20 strata. Facilities were selected using probability proportional to size (PPS). A list of eligible HIV-positive patients who were receiving HIV care at the ART clinic with at least 300 patients was created. In the second stage, a fixed sample of eligible HIV patients were systematically selected and included in the survey. For each day, participants were drawn from the morning and afternoon sessions to minimize selection bias. All HIV + adults (aged between 18 years and above) receiving ART services in the sampled health facility that offered ART services were eligible for inclusion in the study. Pregnant and breastfeeding mothers were excluded from the study. This is because pregnant women experience various gestation problems which could confound the weight measurement. All participants signed the informed concerned form. 2.2 Sample Size Using the prevalence formula, a sample size of 4,620 was estimated as sufficient to estimate an effect size if present in the target population and 5775 when adjusted for 20% non-response. Assuming a prevalence of 50% (conservative estimate), a sample size of 385 produced a two-sided 5% margin of error with a width equal to 0.10 (i.e., the margin of error). To ensure enough representation for age-sex groups to be reported and allow reporting by urban-rural disaggregation, we multiplied the 385 by the number of domains. The number of domains was decided by considering males and females and 4 age groups (18–29, 30–44, 45–59, 60 + years), which gave us 8 groups of the study population. This gave a sample size of 3,080. We further adjusted for a design effect of 1.5 to address the issue of cluster sampling, which gave sample size of 4,620 (i.e., 3,080*1.5). Allowing for a nonresponse of 20%, our final sample size was 5,775 (4620/0.8) HIV + adult patients. 2.3 Sample allocation ` The sample allocation of the 5775 HIV + adult patients was proportional across the 20 strata. Based on a fixed sample take of 35 HIV + adult patients per clinic, 165 clinics (i.e., 5775/35) were selected from the pool of eligible clinics using Probability Proportional to Size. The 165 clinics were allocated to the 20 strata proportionately to size where size was defined as the total number of HIV + adult patients in the clinic. 2.4 Data collection Nurses and laboratory technicians from each sample site were brought to Lusaka, for a two-day intensive training on study procedures. These nurses and laboratory technicians were all employed by the Ministry of Health and offer ART services as part of their routine work. The nurses were assigned the responsibility of screening, sampling, obtaining consent, interviewing, and collecting physical measurements. During the training, the nurses were taken through the consenting processes, data entry and synchronization on Android tablets using the KoboCollect data application tool. The nurses were also reoriented on weight, height, and hip and waist circumference measurements by one of the physicians in the study. Laboratory technicians were oriented on specimen collection, storage in the field and packaging for transportation to the analysis center. Data was collected using a questionnaire with questions adapted from the WHO STEPs surveys and the ZAMPHIA questionnaire. Participants were asked to respond to questions which included basic socio-demographic information (age, sex, and education in years, occupation, household income, marital status, dietary behaviors related to fruit and vegetable consumption, salt/sodium consumption, family history of NCDs, etc.). Responses from participants were recorded by the survey nurse using a handheld digital device (Android Tablet). After obtaining information on participants’ current lifestyle behaviors and other related information, the study further obtained the following physical measurements from each participant. All physical measurements were to the nearest 0.1cm for height, waist and hip circumference and 0.1 kg for weight. For the hip circumference, measurements were taken at the midpoint between the last palpable rib and the top of the iliac crest. The weight of participants was taken using a pre-calibrated weighing and measuring scale and all participants were dressed in light clothing and were barefoot. A stadiometer was used to measure the height of participants. All participants were barefoot when their height was measured. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters. BMI readings were categorized as follows Underweight - 30 kg/m 2 . 2.5 Data analysis Characteristics of participants were summarized using frequency and proportion for categorical variables, while continuous variables were initially checked for normality, if normally distributed then means and standard deviations were reported, otherwise, median and interquartile intervals were reported. The prevalence and corresponding 95% confidence intervals of the non-communicable disease risk factors were estimated for each and both sexes. All analyses were adjusted for survey design features including clustering, sampling weight, and stratification. All analyses were performed using Stata 18 SE (StataCorp, College Station, TX, USA). 2.6 Human ethics and consent to participate The study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Ethics approval was obtained from University of Zambia Biomedical Research Ethics Committee (UNZABREC) [REF. 2460–202]. The protocol was subsequently submitted to the Zambia National Health Research Authority (Ref No: NHRA000010/10/02/2022) for registration and approval. Permission was sought from all participating implementing partners (IPs) to allow their staff to participate in the study. The research was explained to the participants and all participants consented to the study in writing. This study took into account ethical issues in the implementation of the study. The study collected patient-level data including their physical and biomedical measurements and diagnosis. Some of the interview questions that were posed to participants required them to provide information about their behaviors and habits. Such questions might have resulted in some level of discomfort in providing answers to the questions. We dealt with these issues by:(a) obtaining informed consent after a thorough explanation to the sampled participants about the study and its intended objectives as well as their rights; (b) all contact information and other identifiers were expunged from the analysis dataset and the report. Instead, findings were aggregated and reported by district, province, and national levels. This study sought and obtained ethical approval from the University of Zambia Biomedical Research Ethics Committee (REF. 2460–202). The study further sought and obtained research authorization from the National Health Research Authority (NHRA, Ref No: NHRA000010/10/02/2022) and permission from the Permanent Secretary, Ministry of Health to conduct research in the sampled health facilities. All study data are owned by the Ministry of Health as the stewards of the National HIV program. This means that the Ministry of Health has the ultimate responsibility for oversight of all data governance issues. 3.0 Results of the study Demographic characteristics Data analysis was based on a total of 5,204 participants distributed across the 10 provinces of Zambia. Two-thirds (67.2%) of the study participants were females and 44.3% of them were 30–44 years old. The majority (39.5%) of the males were in the age range of 45–59 years old. The majority (42.3%) of the participants were in the age range of 30–44 years old. The results show that 56.3% of the participants were currently married or cohabiting, and 29.8% were either widowed, divorced, or separated. A significant proportion (41.5%) of the participants had a primary education or less. A lower number of females (42.3%) than males (49.8%) had reached secondary school. The majority of females (50.7%) compared to males (37.1%) had a primary education or lower. A high proportion (40%) of the study participants were self-employed, 38% were involved in unpaid work and only 20% were in formal employment. Over three quarters (76.6%) of the study participants lived in urban areas and 23.4% percent in rural areas. Physical measurements The mean BMI was lower (23.6 kg/m 2 ) in rural than in urban (24.2 kg/m 2 ) areas. Results show that 21.3% in rural and 23.2% in urban areas were overweight. About 9.2% in rural and 2.4% in urban communities were obese (Table 1 ). Table 1 Percentage (weighted) of PLHIV aged > 18 years with obesity as a risk factor by location, Zambia. Results for PLHIV aged > 18 years (incl. 95% CI) Both Locations% (CI) Rural % (CI) Urban % (CI) Physical Measurement Mean body mass index - BMI (kg/m 2 ) 24.1 (23.9–24.2) 23.6 (23.4–23.9) 24.2 (24.1–24.4) Percentage who are overweight (BMI ≥ 25 kg/m 2 ) 22.7 (21.6–23.9) 21.3 (19.2–23.5) 23.2 (21.9–24.6) Percentage who are obese (BMI ≥ 30 kg/m 2 ) 11.5 (10.6–12.4) 9.2 (7.8–10.8) 12.4 (11.4–13.5) Physical Measurements by gender (BMI) Both Females Male Mean body mass index - BMI (kg/m 2 ) 24.1 (23.9–24.2) 24.9 (24.7–25.1) 22.4 (22.2–22.6) Percentage who are overweight (BMI ≥ 25 kg/m 2 ) 22.7 (21.6–23.9) 26.4 (24.9–27.9) 15.2 (13.6–17) Percentage who are obese (BMI ≥ 30 kg/m 2 ) 11.5 (10.6–12.4) 14.7 (13.5–15.9) 5 (4-6.1) Although overweight is an emerging problem in people living with HIV, BMI by gender shows overweight was more of a problem for females (26.4%) than males 15.2%. A higher prevalence of obesity was also among females (14.7%) than males (5%) (Table 1 ). Consumption of recommended fruits and vegetables Based on the recommended number of 5 servings for fruits and vegetables, almost three quarters of participants (74.8%) were not in the habit of consuming fruits and vegetables. The proportion of those not consuming the recommended amount was slightly higher for males (76.9%) than females (73.8%). Regarding dietary intake of fruits or vegetables separately, a significantly low proportion (6.4%) consumed vegetables while only 3.3% ate fruits, based on the number of days in a typical week. The dietary consumption of fruits or vegetables was similar between sexes. Consumption of fruits and vegetables was unacceptably low in both rural and urban communities. Nonetheless, there was a slight difference with more participants (76.2%) in urban than in rural (71.2%) who ate less than 5 servings of fruits and/or vegetables on average per day. With regard to sodium intake, 36% reported that they always add salt to their plate as they eat. Slightly more men (40.7%) reported engaging in the practice compared to females (33.6%) each time they had a meal. The study also found that slightly more people in rural areas (39.6%) reported always or often adding salt to their food as they eat compared to those in urban areas (33.9%). An estimated 12.8% reported consuming processed foods in high concentrations (Table 2 ). Table 2 Percentage of PLHIV aged > 18 years, Consumption of Fruits and Vegetables in Zambia Results for adults aged > 18 years (incl. 95% CI) Both Sexes% (CI) Females% (CI) Males% (CI) Mean number of servings of vegetables and fruits consumed by gender Mean number of days vegetables consumed in a typical week 6.4 (6.4–6.5) 6.5 (6.4–6.5) 6.3 (6.2–6.4) Mean number of servings of vegetables consumed on average per day 3.2 (3.1–3.2) 3.2 (3.1–3.3) 3.1 (3-3.2) Mean number of days fruits consumed in a typical week 3.3 (3.2–3.3) 3.2 (3.1–3.3) 3.3 (3.2–3.4) Percentage who ate less than 5 servings of fruits and/or vegetables on average per day 74.8 (73.6–76) 73.8 (72.2–75.3) 76.9 (74.6–79.1) Percentage who always or often add salt or salty sauce to their food before eating or as they are eating 36 (34.8–37.2) 33.6 (32.1–35.2) 40.7 (38.4–43.1) Percentage who always or often eat processed foods high in salt 12.8 (11.9–13.7) 11.7 (10.6–12.8) 15.1 (13.5–16.9) Mean number of servings of vegetables and fruits consumed by location Both %(CI) Rural %(CI) Urban %(CI) Mean number of days vegetables consumed in a typical week 6.4 (6.4–6.5) 6.2 (6.1–6.2) 6.5 (6.5–6.6) Mean number of servings of vegetables consumed on average per day 3.2 (3.1–3.2) 3.4 (3.3–3.5) 3.1 (3-3.1) Percentage who ate less than 5 servings of fruits and/or vegetables on average per day 74.8 (73.6–76) 71.2 (69-73.4) 76.2 (74.7–77.6) Percentage who always or often add salt or salty sauce to their food before eating or as they are eating 36 (34.8–37.2) 39.6 (37.4–41.8) 33.9 (32.5–35.4) Percentage who always or often eat processed foods high in salt 12.8 (11.9–13.7) 17.7 (16-19.6) 10.7 (9.7–11.7) 4.0 Discussion From the results, a higher number of females were accessing HIV services; a lower BMI measurement was found in rural than urban areas; and among men than females. Consumption of fruits and vegetables was unacceptably low in both rural and urban communities. Similarly, in a South African study in Mpumalanga Province, more women than men on ART had high BMI measurements [ 15 ]. Study findings also showed that the majority (85.7%) of the PLHIV on treatment were 30 years old and above. About 40.2% of the female participants and 50.1% of the male participants were 45 years and above. Overall, about 50.64% of the participants (both sexes) were 45 years old and above. This clearly shows that the HIV population in Zambia who are in care is rapidly aging and an increasing number is at the risk of developing one or a combination of NCDs. While men only accounted for 32.84% of the total participants, they also accounted for the largest proportion (61.2%) of participants who were 45 years and above. This study has shown the urgent need to target men in the prevention of NCDs among the PLHIV. Overweight (BMI: 25.0-29.9 kg/m2) and obesity (BMI ≥ 30 kg/m 2 ) are emerging challenges among PLHIV on ART and are living longer [ 12 , 16 ]. According to this study, a significant proportion of PLHIV (22.4%) were overweight, and women (26%) were more affected than males (15%). The average proportion (10.8%) of obesity in the studied population is high, with more women (13.9%) than men (4.5%) classified as obese. In the earlier study (Steps survey), the percentage of respondents that were obese was 7.5%, and women accounted for a higher proportion (12.3%) than men (3%) [ 17 ]. The rising numbers indicate an emerging problem of obesity among PLHIV in Zambia; and calls for strategic policy direction to encourage health promotion activities that may contribute to effective management of obesity among PLHIV. According to WHO recommendations, one should consume at least 400g or 5 portions of fruits and vegetables per day [ 18 ].Based on the recommended intakes, the majority (74.8%) of respondents were not consuming enough fruits and vegetables, and there was little difference between males (76.9%) and females (73.8%). Similarly, in the earlier Steps survey of 2017, a lower number of consumption days for fruits and vegetables was indicated, with little difference recorded between men (2.0 days) and women (2.1 days) [ 17 ]. Managing obesity requires moderation in caloric intake while maintaining an acceptable consumption of fruits and vegetables. According to current and previous studies, most of the Zambian population do not consume the required servings of fruits and vegetables. Adequate intake of fruits and vegetables contributes to reducing the risk of NCDs and supports achieving the required daily dietary intake of fiber [ 17 ]; and helps in weight management. By implication, a low intake of fruits and vegetables indicates a likelihood of inadequate intake of micronutrients, and a dietary challenge in the management of obesity. On the other hand, the high salt intake reported in the study, requires consideration, particularly in the management of obese, hypertensive HIV positive individuals. Although non-communicable diseases are receiving attention in the country, the effects of overweight and obesity among PLHIV on ART are rarely discussed. Apart from dietary intake and physical activity, weight gain may be influenced by the type of ART [ 19 ]. For example, greater weight gain has been reported among those, whose initial therapy was an INSTI-based ART regimen, than those who received PI and NNRTI-based regimen [ 20 ]. Another study reported a 14.2% increase in the risk of hypertension among study participants who were exposed to DTG compared to those who were on other ART regimens [ 21 ]. However, factors influencing weight gain among ART recipients must also be elucidated in relation to benefits, including cardio metabolic risk [ 22 ]. Zambia has a young population and some of those who are HIV positive and on ART may desire to have children. Overweight and obesity impact pregnancy outcomes negatively due to increased risk associated with pregnancy-induced hypertension, gestational diabetes and caesarian sections. In turn these conditions also increase perinatal deaths, prematurity, obesity and heart disease in the offspring [ 23 ]. Within the Zambian context with a young population, the growing problem of overweight and obesity may present an additional difficulty for obese HIV-positive females who wish to have children. This situation also complicates management decisions for those seeking prenatal and antenatal care. 5.0 Limitations of BMI measurement Measurement of weight among overweight and obese individuals requires using appropriate tools to determine fat adiposity. Despite being commonly used in clinical and research settings, anthropometric measurements using BMI assessments do not accurately distinguish between lean and fatty body mass, parameters influenced by age, sex, and race [ 24 ]. PLHIV generally have more visceral adipose tissue (VAT) than their counterparts without HIV. Accurate estimation of subcutaneous adipose tissue (SAT) and VAT can allow for better prediction of cardiometabolic complications than BMI in PLHIV. Several anthropometric indices are available for characterizing adiposity in PLHIV. Where possible, Magnetic resonance imaging and computed tomography (CT) are advised for assessing SAT and VAT, particularly in research settings [ 25 ]. 6.0 Conclusion Overweight and obesity are emerging problems among individuals on ART living longer with HIV. Based on WHO recommendations of daily intake of five fruit and vegetable servings per day, the majority of participants in the study were not consuming enough fruits and vegetables. This study reflects a low consumption of fruits and vegetables and high salt intake. The status quo complicates the dietary management of overweight and obesity among HIV positive individuals in Zambia; and a multi-pronged approach is required to adequately support them. Recommendations In order to address the emerging problem of obesity among people living with HIV, it is important to incorporate screening for overweight and obese individuals receiving routine reproductive health care services. This means that properly calibrated height and weighing scales, including hip and waist circumference measuring tapes, should be part of the basic equipment at each health facility regardless of size. These measurements should be available to support the calculation of BMI during routine practice. Any patient found to have a BMI that is above normal should be adequately advised about the need for lifestyle change. There is a need to raise awareness among policymakers to take obesity and overweight into account when drafting policies. Overweight and obesity should be regarded as a serious implication in the development of cardio-metabolic conditions. Increased physical activity and appropriate diet recommendations through awareness campaigns must be strengthened. There is an urgent need to raise awareness about the influence of specific ART drugs such as DTG that contribute to weight gain and the risk of obesity among PLHIV. People at high risk of being obese due to such drugs should be counselled and managed accordingly. There is a need to advocate for methods that accurately measure fat adiposity to adequately support the management of those who are overweight or obese and living with HIV. Declarations Funding Declaration This manuscript is a result of research conducted in Zambia in 2023. The Global Fund provided financial support through Zambia’s Ministry of Health to the School of Public Health, University of Zambia, to conduct the study. Clinical Trial number: Not applicable Consent to Publish The authors have acknowledged that they have no conflict of interest in submitting this manuscript to the Discover Public Health Journal for publication. References UNAIDS. 2023. Global HIV & AIDS statistics - Fact Sheet [Online]. Available: https://www.unaids.org/en/resources/fact-sheet. Accessed 23 March 2025. 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Current HIV/AIDS reports , 15 (1), 60–71. https://doi.org/10.1007/s11904-018-0378-z. AMANO, S. U., COHEN, J. L., VANGALA, P., TENCEROVA, M., NICOLORO, S. M., YAWE, J. C., SHEN, Y., CZECH, M. P. & AOUADI, M. 2014. Local proliferation of macrophages contributes to obesity-associated adipose tissue inflammation. Cell Metab, 19 , 162-171. DOI: 10.1016/j.cmet.2013.11.017. KOETHE, J. R., JENKINS, C. A., LAU, B., SHEPHERD, B. E., JUSTICE, A. C., TATE, J. P., BUCHACZ, K., NAPRAVNIK, S., MAYOR, A. M., HORBERG, M. A., BLASHILL, A. J., WILLIG, A., WESTER, C. W., SILVERBERG, M. J., GILL, J., THORNE, J. E., KLEIN, M., ERON, J. J., KITAHATA, M. M., STERLING, T. R. & MOORE, R. D. 2016. Rising Obesity Prevalence and Weight Gain Among Adults Starting Antiretroviral Therapy in the United States and Canada. AIDS Res Hum Retroviruses, 32 , 50-8. DOI: 10.1089/aid.2015.0147. COUTURIER, J., WINCHESTER, L. C., SULIBURK, J. W., WILKERSON, G. K., PODANY, A. T., AGARWAL, N., XUAN CHUA, C. Y., NEHETE, P. N., NEHETE, B. P., GRATTONI, A., SASTRY, K. J., FLETCHER, C. V., LAKE, J. E., BALASUBRAMANYAM, A. & LEWIS, D. E. 2018. Adipocytes impair efficacy of antiretroviral therapy. Antiviral Res, 154 , 140-148. DOI: 10.1016/j.antiviral.2018.04.002. TOULOUMI, G., KALPOURTZI, N., PAPASTAMOPOULOS, V., PAPARIZOS, V., ADAMIS, G., ANTONIADOU, A., CHINI, M., KARAKOSTA, A., MAKRILAKIS, K., GAVANA, M., VANTARAKIS, A., PSICHOGIOU, M., METALLIDIS, S., SIPSAS, N. V., SAMBATAKOU, H., HADJICHRISTODOULOU, C., VOULGARI, P. V., CHRYSOS, G., GOGOS, C., CHLOUVERAKIS, G., … AMACS AND EMENO (2020). Cardiovascular risk factors in HIV infected individuals: Comparison with general adult control population in Greece. PloS one , 15 (3), e0230730. https://doi.org/10.1371/journal.pone.0230730. BLOOMFIELD, G. S., KHAZANIE, P., MORRIS, A., RABADÁN-DIEHL, C., BENJAMIN, L. A., MURDOCH, D., RADCLIFF, V. S., VELAZQUEZ, E. J. & HICKS, C. 2014. HIV and noncommunicable cardiovascular and pulmonary diseases in low- and middle-income countries in the ART era: what we know and best directions for future research. J Acquir Immune Defic Syndr, 67 Suppl 1 , S40-53. doi:10.1097/QAI.0000000000000257. BARES, S. H., SMEATON, L. M., XU, AI., GODFREY, C. & MCCOMSEY, G. A. 2018. HIV-Infected Women Gain More Weight than HIV-Infected Men Following the Initiation of Antiretroviral Therapy. J Womens Health (Larchmt), 27 , 1162-1169. DOI: 10.1089/jwh.2017.6717. PENGPID, S. & PELTZER, K. 2020. Prevalence and correlates of multiple non-communicable disease risk factors among adults in Zambia: results of the first national STEPS survey in 2017. Pan Afr Med J, 37 , 265. DOI: 10.11604/pamj.2020.37.265.25038. WHO. 2020. Healthy Diet [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/healthy-diet [Accessed 05/08/2025 2025]. BARES, S. H., SMEATON, L. M., XU, A., GODFREY, C. & MCCOMSEY, G. A. 2018. HIV-Infected Women Gain More Weight than HIV-Infected Men Following the Initiation of Antiretroviral Therapy. J Womens Health (Larchmt), 27 , 1162-1169. DOI: 10.1089/jwh.2017.6717. VENTER, W. D. F., MOORHOUSE, M., SOKHELA, S., FAIRLIE, L., MASHABANE, N., MASENYA, M., SERENATA, C., AKPOMIEMIE, G., QAVI, A., CHANDIWANA, N., NORRIS, S., CHERSICH, M., CLAYDEN, P., ABRAMS, E., ARULAPPAN, N., VOS, A., MCCANN, K., SIMMONS, B. & HILL, A. 2019. Dolutegravir plus Two Different Prodrugs of Tenofovir to Treat HIV. N Engl J Med, 381 , 803-815. DOI: 10.1056/NEJMoa1902824 . BRENNAN, A. T., NATTEY, C., KILEEL, E. M., ROSEN, S., MASKEW, M., STOKES, A. C., FOX, M. P. & VENTER, W. D. F. 2023. Change in body weight and risk of hypertension after switching from efavirenz to dolutegravir in adults living with HIV: evidence from routine care in Johannesburg, South Africa. EClinicalMedicine, 57 , 101836. DOI: 10.1016/j.eclinm.2023.101836. DIMALA, C. A., NGU, R. C., KADIA, B. M., TIANYI, F. L., & CHOUKEM, S. P. 2018. Markers of adiposity in HIV/AIDS patients: Agreement between waist circumference, waist-to-hip ratio, waist-to-height ratio and body mass index. PloS one , 13 (3), e0194653. https://doi.org/10.1371/journal.pone.0194653. VATS, H., SAXENA, R., SACHDEVA, M. P., WALIA, G. K. & GUPTA, V. 2021. Impact of maternal pre-pregnancy body mass index on maternal, fetal and neonatal adverse outcomes in the worldwide populations: A systematic review and meta-analysis. Obes Res Clin Pract, 15 , 536-545. DOI: 10.1016/j.orcp.2021.10.005 NUTTALL, F. Q. 2015. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today, 50 , 117-128. DOI: 10.1097/NT.0000000000000092. KLEIN, S., ALLISON, D. B., HEYMSFIELD, S. B., KELLEY, D. E., LEIBEL, R. L., NONAS, C. & KAHN, R. 2012. Waist circumference and cardiometabolic risk: a consensus statement from shaping America's health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Obesity , 15,(5) , 1061-1067. https://doi.org/10.1038/oby.2007.632. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Zambia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe increasing occurrence of obesity as one of the non-communicable diseases among People living with Human Immune Virus (PLHIV) presents a challenge in management. The introduction and scaling up of access (70%) to Antiretroviral Therapy (ART) among PLHIV has led to reduction of AIDS related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Zambia Population-based HIV Impact Assessment (ZAMPHIA) report shows that 89% of PLHIV know their status; 96% of these are on ART treatment; and 98% of those on ART are virally suppressed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to improved access, PLHIV are living longer with improved quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Living longer is associated with new challenges. In particular, there is an increased occurrence of non-communicable diseases (NCDs) among many PLHIV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A Zambian study found that age, female gender, dolutegravir (DTG) based regimen, hip circumference, T-lymphocyte count, high-sensitivity c-reactive protein and fasting blood sugar were strongly associated with metabolic syndrome among PLHIV [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Zambian health system is not equipped to deal with HIV associated complications. By implication, the current focus on HIV control requires allocating significant resources to the detection, prevention, and control of NCDs especially among the PLHIV. The associated increase in non-communicable diseases such as cancers and cardiovascular diseases among HIV positive individuals is of particular concern [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For Zambia, morbidity and mortality associated with NCDs, among the PLHIV may contribute to reducing the achievements made towards the fight against HIV.\u003c/p\u003e\u003cp\u003eImproved access to combination ART has led to a decline in wasting and resulted in increased numbers of overweight and obese people living with HIV [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Excess adiposity observed in obese individuals on ART is associated with metabolic diseases such as diabetes mellitus, liver diseases, cardiovascular disease and neurocognitive impairment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Being HIV positive and carrying excess adipose tissue is implicated in systemic inflammation, arising partly due to changes in the adipose tissue and immune cell profile [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Increased overweight (BMI: 25.0\u0026ndash;29.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) has been observed among PLHIV initiated on ART [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The type of ART may influence weight gain. However, there is limited data to explain the gravity of obesity among PLHIV receiving ART by income or location (high-density or rural communities). For example, in vitro experiments in Houston-Texas in the USA, greater weight was reported among those, whose initial therapy was an Integrase Strand Transfer Inhibitor (INSTI) based ART regimen, in comparison to those who received Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) and Protease Inhibitor (PI) based regimens. Nonetheless, mechanisms influencing weight gain in those on ART need to be explained in context with regard to efficacy, viral suppression and reduction in metabolic disturbances and HIV infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenerally, investments in health systems strengthening and raising per-capita incomes in low-and -middle-income Countries (LMICs) are contributing to the epidemiological transition from communicable diseases in relation to ageing, lifestyle and NCDs. This is leading to a double burden, whereby communicable diseases are slowly decreasing while NCDs are rapidly increasing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Meanwhile, LMIC\u0026rsquo;s health systems by design, are still focused on responding to communicable diseases with minimal investments in chronic care. Most NCDs share common traditional risk factors such as poor nutrition and physical inactivity. Considering the current limitations in the Zambian health system, the growing number of obese individuals among PLHIV is cause for concern. NCDs including obesity are expensive to manage, if not addressed, there is a possibility of diminishing the already scarce resources designated for national development; and undermine social\u0026ndash;economic progress in the country.\u003c/p\u003e\u003cp\u003eAlthough Zambia has conducted several population-based studies over the years such as the demographic and health surveys, and the Zambia Population-Based HIV Impact Assessment (ZAMPHIA), these studies have mainly focused on HIV prevalence and associated risk factors, and not on HIV and Non-Communicable Diseases. Similarly, the data from the first STEPS Survey for Zambia did not include relevant data on NCDs and associated risk factors among PLHIV. While the Electronic data system used in Zambia (SMARTCARE) collects information on various HIV-related Indicators, it remains unreliable on the quality of data collected and has incomplete information on NCDs, including high blood pressure, diabetes, dyslipidemia or obesity. Currently, there is limited nationwide data on the prevalence of non-communicable diseases such as obesity and intake of fruits and vegetables among the HIV-positive population. Understanding the burden of obesity and dietary intakes among PLHIV will help design appropriate interventions to address the growing complexity of managing co-morbidities of HIV and obesity in Zambia. The current study estimated the prevalence of obesity and adherence to the recommended dietary intake of fruits and vegetables in the diet.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003e A cross sectional design was followed. A stratified two-stage health facility survey was conducted between 26/10/2023 and 30/11/2023 in all ten provinces of Zambia. Initially, facilities were stratified into province and rural-urban classification into 20 strata. Facilities were selected using probability proportional to size (PPS). A list of eligible HIV-positive patients who were receiving HIV care at the ART clinic with at least 300 patients was created. In the second stage, a fixed sample of eligible HIV patients were systematically selected and included in the survey. For each day, participants were drawn from the morning and afternoon sessions to minimize selection bias. All HIV\u0026thinsp;+\u0026thinsp;adults (aged between 18 years and above) receiving ART services in the sampled health facility that offered ART services were eligible for inclusion in the study. Pregnant and breastfeeding mothers were excluded from the study. This is because pregnant women experience various gestation problems which could confound the weight measurement. All participants signed the informed concerned form.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sample Size\u003c/h2\u003e\u003cp\u003eUsing the prevalence formula, a sample size of 4,620 was estimated as sufficient to estimate an effect size if present in the target population and 5775 when adjusted for 20% non-response. Assuming a prevalence of 50% (conservative estimate), a sample size of 385 produced a two-sided 5% margin of error with a width equal to 0.10 (i.e., the margin of error). To ensure enough representation for age-sex groups to be reported and allow reporting by urban-rural disaggregation, we multiplied the 385 by the number of domains. The number of domains was decided by considering males and females and 4 age groups (18\u0026ndash;29, 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026thinsp;+\u0026thinsp;years), which gave us 8 groups of the study population. This gave a sample size of 3,080. We further adjusted for a design effect of 1.5 to address the issue of cluster sampling, which gave sample size of 4,620 (i.e., 3,080*1.5). Allowing for a nonresponse of 20%, our final sample size was 5,775 (4620/0.8) HIV\u0026thinsp;+\u0026thinsp;adult patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample allocation\u003c/h2\u003e\u003cp\u003e` The sample allocation of the 5775 HIV\u0026thinsp;+\u0026thinsp;adult patients was proportional across the 20 strata. Based on a fixed sample take of 35 HIV\u0026thinsp;+\u0026thinsp;adult patients per clinic, 165 clinics (i.e., 5775/35) were selected from the pool of eligible clinics using Probability Proportional to Size. The 165 clinics were allocated to the 20 strata proportionately to size where size was defined as the total number of HIV\u0026thinsp;+\u0026thinsp;adult patients in the clinic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data collection\u003c/h2\u003e\u003cp\u003eNurses and laboratory technicians from each sample site were brought to Lusaka, for a two-day intensive training on study procedures. These nurses and laboratory technicians were all employed by the Ministry of Health and offer ART services as part of their routine work. The nurses were assigned the responsibility of screening, sampling, obtaining consent, interviewing, and collecting physical measurements. During the training, the nurses were taken through the consenting processes, data entry and synchronization on Android tablets using the KoboCollect data application tool. The nurses were also reoriented on weight, height, and hip and waist circumference measurements by one of the physicians in the study. Laboratory technicians were oriented on specimen collection, storage in the field and packaging for transportation to the analysis center.\u003c/p\u003e\u003cp\u003eData was collected using a questionnaire with questions adapted from the WHO STEPs surveys and the ZAMPHIA questionnaire. Participants were asked to respond to questions which included basic socio-demographic information (age, sex, and education in years, occupation, household income, marital status, dietary behaviors related to fruit and vegetable consumption, salt/sodium consumption, family history of NCDs, etc.). Responses from participants were recorded by the survey nurse using a handheld digital device (Android Tablet).\u003c/p\u003e\u003cp\u003eAfter obtaining information on participants\u0026rsquo; current lifestyle behaviors and other related information, the study further obtained the following physical measurements from each participant. All physical measurements were to the nearest 0.1cm for height, waist and hip circumference and 0.1 kg for weight. For the hip circumference, measurements were taken at the midpoint between the last palpable rib and the top of the iliac crest. The weight of participants was taken using a pre-calibrated weighing and measuring scale and all participants were dressed in light clothing and were barefoot. A stadiometer was used to measure the height of participants. All participants were barefoot when their height was measured. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters. BMI readings were categorized as follows Underweight - \u0026lt; 18.5 kg/m\u003csup\u003e2\u003c/sup\u003e; Normal weight \u0026minus;\u0026thinsp;18.5 to 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e; Overweight \u0026minus;\u0026thinsp;25 to 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e; and Obese - \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data analysis\u003c/h2\u003e\u003cp\u003eCharacteristics of participants were summarized using frequency and proportion for categorical variables, while continuous variables were initially checked for normality, if normally distributed then means and standard deviations were reported, otherwise, median and interquartile intervals were reported. The prevalence and corresponding 95% confidence intervals of the non-communicable disease risk factors were estimated for each and both sexes. All analyses were adjusted for survey design features including clustering, sampling weight, and stratification. All analyses were performed using Stata 18 SE (StataCorp, College Station, TX, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Human ethics and consent to participate\u003c/h2\u003e\u003cp\u003e The study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Ethics approval was obtained from University of Zambia Biomedical Research Ethics Committee (UNZABREC) [REF. 2460\u0026ndash;202]. The protocol was subsequently submitted to the Zambia National Health Research Authority (Ref No: NHRA000010/10/02/2022) for registration and approval. Permission was sought from all participating implementing partners (IPs) to allow their staff to participate in the study. The research was explained to the participants and all participants consented to the study in writing.\u003c/p\u003e\u003cp\u003eThis study took into account ethical issues in the implementation of the study. The study collected patient-level data including their physical and biomedical measurements and diagnosis. Some of the interview questions that were posed to participants required them to provide information about their behaviors and habits. Such questions might have resulted in some level of discomfort in providing answers to the questions. We dealt with these issues by:(a) obtaining informed consent after a thorough explanation to the sampled participants about the study and its intended objectives as well as their rights; (b) all contact information and other identifiers were expunged from the analysis dataset and the report. Instead, findings were aggregated and reported by district, province, and national levels. This study sought and obtained ethical approval from the University of Zambia Biomedical Research Ethics Committee (REF. 2460\u0026ndash;202). The study further sought and obtained research authorization from the National Health Research Authority (NHRA, Ref No: NHRA000010/10/02/2022) and permission from the Permanent Secretary, Ministry of Health to conduct research in the sampled health facilities. All study data are owned by the Ministry of Health as the stewards of the National HIV program. This means that the Ministry of Health has the ultimate responsibility for oversight of all data governance issues.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results of the study","content":"\u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003cp\u003eData analysis was based on a total of 5,204 participants distributed across the 10 provinces of Zambia. Two-thirds (67.2%) of the study participants were females and 44.3% of them were 30\u0026ndash;44 years old. The majority (39.5%) of the males were in the age range of 45\u0026ndash;59 years old. The majority (42.3%) of the participants were in the age range of 30\u0026ndash;44 years old. The results show that 56.3% of the participants were currently married or cohabiting, and 29.8% were either widowed, divorced, or separated. A significant proportion (41.5%) of the participants had a primary education or less. A lower number of females (42.3%) than males (49.8%) had reached secondary school. The majority of females (50.7%) compared to males (37.1%) had a primary education or lower. A high proportion (40%) of the study participants were self-employed, 38% were involved in unpaid work and only 20% were in formal employment. Over three quarters (76.6%) of the study participants lived in urban areas and 23.4% percent in rural areas.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePhysical measurements\u003c/strong\u003e\u003cp\u003eThe mean BMI was lower (23.6 kg/m\u003csup\u003e2\u003c/sup\u003e) in rural than in urban (24.2 kg/m\u003csup\u003e2\u003c/sup\u003e) areas. Results show that 21.3% in rural and 23.2% in urban areas were overweight. About 9.2% in rural and 2.4% in urban communities were obese (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage (weighted) of PLHIV aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years with obesity as a risk factor by location, Zambia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eResults for PLHIV aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years (incl. 95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eBoth Locations% (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRural % (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrban % (CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003ePhysical Measurement\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean body mass index - BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e24.1 (23.9\u0026ndash;24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.6 (23.4\u0026ndash;23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.2 (24.1\u0026ndash;24.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage who are overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e22.7 (21.6\u0026ndash;23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.3 (19.2\u0026ndash;23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.2 (21.9\u0026ndash;24.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage who are obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e11.5 (10.6\u0026ndash;12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.2 (7.8\u0026ndash;10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.4 (11.4\u0026ndash;13.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Measurements by gender (BMI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean body mass index - BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e24.1 (23.9\u0026ndash;24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e24.9 (24.7\u0026ndash;25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.4 (22.2\u0026ndash;22.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage who are overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e22.7 (21.6\u0026ndash;23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e26.4 (24.9\u0026ndash;27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.2 (13.6\u0026ndash;17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage who are obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e11.5 (10.6\u0026ndash;12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14.7 (13.5\u0026ndash;15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (4-6.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough overweight is an emerging problem in people living with HIV, BMI by gender shows overweight was more of a problem for females (26.4%) than males 15.2%. A higher prevalence of obesity was also among females (14.7%) than males (5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsumption of recommended fruits and vegetables\u003c/strong\u003e\u003cp\u003eBased on the recommended number of 5 servings for fruits and vegetables, almost three quarters of participants (74.8%) were not in the habit of consuming fruits and vegetables. The proportion of those not consuming the recommended amount was slightly higher for males (76.9%) than females (73.8%). Regarding dietary intake of fruits or vegetables separately, a significantly low proportion (6.4%) consumed vegetables while only 3.3% ate fruits, based on the number of days in a typical week. The dietary consumption of fruits or vegetables was similar between sexes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eConsumption of fruits and vegetables was unacceptably low in both rural and urban communities. Nonetheless, there was a slight difference with more participants (76.2%) in urban than in rural (71.2%) who ate less than 5 servings of fruits and/or vegetables on average per day. With regard to sodium intake, 36% reported that they always add salt to their plate as they eat. Slightly more men (40.7%) reported engaging in the practice compared to females (33.6%) each time they had a meal. The study also found that slightly more people in rural areas (39.6%) reported always or often adding salt to their food as they eat compared to those in urban areas (33.9%). An estimated 12.8% reported consuming processed foods in high concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage of PLHIV aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years, Consumption of Fruits and Vegetables in Zambia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResults for adults aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years (incl. 95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBoth Sexes% (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemales% (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMales% (CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMean number of servings of vegetables and fruits consumed by gender\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean number of days vegetables consumed in a typical week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6.4 (6.4\u0026ndash;6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.5 (6.4\u0026ndash;6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3 (6.2\u0026ndash;6.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean number of servings of vegetables consumed on average per day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.2 (3.1\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (3.1\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1 (3-3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean number of days fruits consumed in a typical week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.3 (3.2\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (3.1\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3 (3.2\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage who ate less than 5 servings of fruits and/or vegetables on average per day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e74.8 (73.6\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.8 (72.2\u0026ndash;75.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.9 (74.6\u0026ndash;79.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage who always or often add salt or salty sauce to their food before eating or as they are eating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e36 (34.8\u0026ndash;37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.6 (32.1\u0026ndash;35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.7 (38.4\u0026ndash;43.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage who always or often eat processed foods high in salt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12.8 (11.9\u0026ndash;13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.7 (10.6\u0026ndash;12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.1 (13.5\u0026ndash;16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean number of servings of vegetables and fruits consumed by location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBoth %(CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRural %(CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUrban %(CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean number of days vegetables consumed in a typical week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4 (6.4\u0026ndash;6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2 (6.1\u0026ndash;6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.5 (6.5\u0026ndash;6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean number of servings of vegetables consumed on average per day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2 (3.1\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4 (3.3\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1 (3-3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage who ate less than 5 servings of fruits and/or vegetables on average per day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.8 (73.6\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.2 (69-73.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.2 (74.7\u0026ndash;77.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage who always or often add salt or salty sauce to their food before eating or as they are eating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (34.8\u0026ndash;37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.6 (37.4\u0026ndash;41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.9 (32.5\u0026ndash;35.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePercentage who always or often eat processed foods high in salt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.8 (11.9\u0026ndash;13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.7 (16-19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.7 (9.7\u0026ndash;11.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eFrom the results, a higher number of females were accessing HIV services; a lower BMI measurement was found in rural than urban areas; and among men than females. Consumption of fruits and vegetables was unacceptably low in both rural and urban communities. Similarly, in a South African study in Mpumalanga Province, more women than men on ART had high BMI measurements [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Study findings also showed that the majority (85.7%) of the PLHIV on treatment were 30 years old and above. About 40.2% of the female participants and 50.1% of the male participants were 45 years and above. Overall, about 50.64% of the participants (both sexes) were 45 years old and above. This clearly shows that the HIV population in Zambia who are in care is rapidly aging and an increasing number is at the risk of developing one or a combination of NCDs. While men only accounted for 32.84% of the total participants, they also accounted for the largest proportion (61.2%) of participants who were 45 years and above. This study has shown the urgent need to target men in the prevention of NCDs among the PLHIV.\u003c/p\u003e\u003cp\u003eOverweight (BMI: 25.0-29.9 kg/m2) and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) are emerging challenges among PLHIV on ART and are living longer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. According to this study, a significant proportion of PLHIV (22.4%) were overweight, and women (26%) were more affected than males (15%). The average proportion (10.8%) of obesity in the studied population is high, with more women (13.9%) than men (4.5%) classified as obese. In the earlier study (Steps survey), the percentage of respondents that were obese was 7.5%, and women accounted for a higher proportion (12.3%) than men (3%) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The rising numbers indicate an emerging problem of obesity among PLHIV in Zambia; and calls for strategic policy direction to encourage health promotion activities that may contribute to effective management of obesity among PLHIV.\u003c/p\u003e\u003cp\u003eAccording to WHO recommendations, one should consume at least 400g or 5 portions of fruits and vegetables per day [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Based on the recommended intakes, the majority (74.8%) of respondents were not consuming enough fruits and vegetables, and there was little difference between males (76.9%) and females (73.8%). Similarly, in the earlier Steps survey of 2017, a lower number of consumption days for fruits and vegetables was indicated, with little difference recorded between men (2.0 days) and women (2.1 days) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Managing obesity requires moderation in caloric intake while maintaining an acceptable consumption of fruits and vegetables. According to current and previous studies, most of the Zambian population do not consume the required servings of fruits and vegetables. Adequate intake of fruits and vegetables contributes to reducing the risk of NCDs and supports achieving the required daily dietary intake of fiber [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; and helps in weight management. By implication, a low intake of fruits and vegetables indicates a likelihood of inadequate intake of micronutrients, and a dietary challenge in the management of obesity. On the other hand, the high salt intake reported in the study, requires consideration, particularly in the management of obese, hypertensive HIV positive individuals.\u003c/p\u003e\u003cp\u003eAlthough non-communicable diseases are receiving attention in the country, the effects of overweight and obesity among PLHIV on ART are rarely discussed. Apart from dietary intake and physical activity, weight gain may be influenced by the type of ART [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, greater weight gain has been reported among those, whose initial therapy was an INSTI-based ART regimen, than those who received PI and NNRTI-based regimen [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another study reported a 14.2% increase in the risk of hypertension among study participants who were exposed to DTG compared to those who were on other ART regimens [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, factors influencing weight gain among ART recipients must also be elucidated in relation to benefits, including cardio metabolic risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Zambia has a young population and some of those who are HIV positive and on ART may desire to have children. Overweight and obesity impact pregnancy outcomes negatively due to increased risk associated with pregnancy-induced hypertension, gestational diabetes and caesarian sections. In turn these conditions also increase perinatal deaths, prematurity, obesity and heart disease in the offspring [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Within the Zambian context with a young population, the growing problem of overweight and obesity may present an additional difficulty for obese HIV-positive females who wish to have children. This situation also complicates management decisions for those seeking prenatal and antenatal care.\u003c/p\u003e"},{"header":"5.0 Limitations of BMI measurement","content":"\u003cp\u003eMeasurement of weight among overweight and obese individuals requires using appropriate tools to determine fat adiposity. Despite being commonly used in clinical and research settings, anthropometric measurements using BMI assessments do not accurately distinguish between lean and fatty body mass, parameters influenced by age, sex, and race [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. PLHIV generally have more visceral adipose tissue (VAT) than their counterparts without HIV. Accurate estimation of subcutaneous adipose tissue (SAT) and VAT can allow for better prediction of cardiometabolic complications than BMI in PLHIV. Several anthropometric indices are available for characterizing adiposity in PLHIV. Where possible, Magnetic resonance imaging and computed tomography (CT) are advised for assessing SAT and VAT, particularly in research settings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"6.0 Conclusion","content":"\u003cp\u003eOverweight and obesity are emerging problems among individuals on ART living longer with HIV. Based on WHO recommendations of daily intake of five fruit and vegetable servings per day, the majority of participants in the study were not consuming enough fruits and vegetables. This study reflects a low consumption of fruits and vegetables and high salt intake. The status quo complicates the dietary management of overweight and obesity among HIV positive individuals in Zambia; and a multi-pronged approach is required to adequately support them.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRecommendations\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn order to address the emerging problem of obesity among people living with HIV, it is important to incorporate screening for overweight and obese individuals receiving routine reproductive health care services. This means that properly calibrated height and weighing scales, including hip and waist circumference measuring tapes, should be part of the basic equipment at each health facility regardless of size. These measurements should be available to support the calculation of BMI during routine practice. Any patient found to have a BMI that is above normal should be adequately advised about the need for lifestyle change. There is a need to raise awareness among policymakers to take obesity and overweight into account when drafting policies. Overweight and obesity should be regarded as a serious implication in the development of cardio-metabolic conditions. Increased physical activity and appropriate diet recommendations through awareness campaigns must be strengthened. There is an urgent need to raise awareness about the influence of specific ART drugs such as DTG that contribute to weight gain and the risk of obesity among PLHIV. People at high risk of being obese due to such drugs should be counselled and managed accordingly. There is a need to advocate for methods that accurately measure fat adiposity to adequately support the management of those who are overweight or obese and living with HIV.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is a result of research conducted in Zambia in 2023. The Global Fund provided financial support through Zambia\u0026rsquo;s Ministry of Health to the School of Public Health, \u0026nbsp;University of Zambia, to conduct the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have acknowledged that they have no conflict of interest in submitting this manuscript to the Discover Public Health Journal for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUNAIDS. 2023. \u003cem\u003eGlobal HIV \u0026amp; AIDS statistics - Fact Sheet \u003c/em\u003e[Online]. Available: https://www.unaids.org/en/resources/fact-sheet. 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Obesity \u003cem\u003e,\u003c/em\u003e 15,(5)\u003cstrong\u003e,\u003c/strong\u003e 1061-1067. https://doi.org/10.1038/oby.2007.632. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Zambia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, ART, Fruits, Vegetables, Consumption","lastPublishedDoi":"10.21203/rs.3.rs-7851833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7851833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe problem of obesity among People Living with HIV (PLHIV) receiving Antiretroviral therapy (ART) is not yet explained with regard to gender, rural or urban location. This study assessed the prevalence of obesity and intake of fruits and vegetables to explain the problem within the Zambian context. Participants above 18 years old from health facilities with a minimum of 300 registered HIV patients were recruited and interviewed. Stata 18 SE (Stata Corp, College Station, TX, USA) was used for analysis. Participants\u0026rsquo; characteristics were summarized using frequency and proportion for categorical variables while continuous variables were firstly checked for normality, if normally distributed then means and standard deviation were reported. The prevalence and corresponding 95% confidence intervals of risk factors were estimated. Over one tenth (10.8%) of the participants were obese. Women had a higher prevalence of overweight (26.4%) compared to men (15.2%) and more females (13.9%) were obese than males (4.5%). BMI was lower in rural (23.6 kg/m\u003csup\u003e2\u003c/sup\u003e) than in urban areas (24.2 kg/m\u003csup\u003e2\u003c/sup\u003e). Overweight was slightly lower in rural (21.3%) than urban communities (23.2%). Similarly, obesity was higher in urban (12.4%) than in rural areas (9.2%). The majority (74.8%) of respondents were not consuming enough fruits and vegetables with little difference between males (76.9%) and females (73.8%). The limited fruit and vegetable consumption and high salt intake indicates a dietary challenge in the management of obese HIV positive patients. It is recommended that health promotion should be incorporated in routine screening for overweight and obese individuals receiving ART. Individuals found with BMI above normal should be advised on lifestyle changes in relation to medication and diet.\u003c/p\u003e","manuscriptTitle":"Obesity and adherence to recommended fruits and vegetable intake among the HIV-positive population in Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 14:25:20","doi":"10.21203/rs.3.rs-7851833/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5970ff0f-09e1-48b8-9bfb-fb162f200a34","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56231549,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2025-10-15T14:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 14:25:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7851833","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7851833","identity":"rs-7851833","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}