Healthcare seeking behaviour for common infectious syndromes among people in three administrative regions of Johannesburg, South Africa, 2015: a cross-sectional study

Healthcare seeking behaviour for common infectious syndromes among people in three administrative regions of Johannesburg, South Africa, 2015: a cross-sectional study

Relebogile Mapuroma^1,2, Cheryl Cohen^3,4,&, Lazarus Kuonza^1,2, Alfred Musekiwa^2,5, Stefano Tempia^3,6,7,8, Akhona Tshangela³, Claire von Mollendorf^3,4

 

¹School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa, ²South African Field Epidemiology Program, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa, ³Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa, ⁴School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, ⁵Wits Reproductive Health & HIV Institute (Wits RHI), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, ⁶US Centers for Disease Control and Prevention, Pretoria, South Africa, ⁷Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, ⁸MassGenics, Duluth, Georgia, USA

 

 

^&Corresponding author
Cheryl Cohen, Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa

 

 

 

 

In South Africa, tuberculosis, influenza and pneumonia were among the top ten causes of death in all age groups in 2014-2016 [1]. In 2016, of 456,612 total deaths reported, tuberculosis was responsible for 29,680 (6.5%), and pneumonia and influenza for 19,634 (4.3%) [1]. In children under 15 years of age, intestinal infectious diseases and central nervous system inflammatory diseases were in the top ten causes of deaths in 2016 [1]. Since 2009, the National Institute for Communicable Diseases (NICD) has conducted active, prospective, hospital-based surveillance for pneumonia in five of South Africa's nine provinces to describe its epidemiology, characterise causative pathogens and prepare for pandemics and outbreaks [2]. From 2012 the NICD conducted prospective surveillance for persons with influenza-like illness (ILI) at outpatient clinics located in the catchment areas of two pneumonia surveillance sites [3]. Standard World Health Organization case definitions were used for ILI and severe acute respiratory illness surveillance, with respiratory samples tested using a multiplex real-time reverse transcription-polymerase chain reaction [4]. The NICD also has active laboratory-based surveillance which monitors several pathogens including those causing diarrhoea and meningitis [5]. The disadvantage of facility-based surveillance programs is that they only capture information for people seeking healthcare at facilities conducting surveillance. This can lead to inaccurate interpretation of surveillance data as other people are missed [6]. South Africa has a health information system but this also captures information at a facility level. A healthcare utilisation survey (HUS) captures information on health seeking behaviour that is missed by surveillance programs [7]. Several HUSs have been conducted in different countries to complement surveillance program data [8-10]. As communities differ, it is important to understand healthcare seeking patterns in different settings [9]. Different individual and healthcare provider factors may influence healthcare seeking behaviour [11, 12]. Knowledge of healthcare utilisation behaviour within communities is useful when developing health policies and implementing health programs. The study objective was to describe healthcare seeking behaviour for individuals with common infectious syndromes in the catchment areas of two sentinel surveillance hospitals in Johannesburg, South Africa.

 

 

Study design: we performed a cross-sectional community-based HUS using a one-stage cluster design, with households as the sampling unit, similar to previously described methods [9]. We collected data on three respiratory syndromes (tuberculosis, ILI and pneumonia), meningitis and diarrhoea in 27 suburbs in regions A, B and C of Johannesburg, South Africa from August to November 2015.

 

Study setting: Johannesburg, the largest city in South Africa, has a population size of 4,4 million [13]. It has seven culturally and socio-economically diverse administrative regions (A-G). In 2011 region A had a population of around 609,396, region B 337,053 and region C 384,858 [13, 14]. Two public hospitals, Helen Joseph and Rahima Moosa Mother and Child Hospitals, situated in Region B, are involved in the NICD laboratory-based surveillance program [5] and were initiated as pneumonia surveillance sites in 2014 (referred to hereafter as sentinel hospitals). The survey was conducted in specific suburbs of regions A, B and C, which are catchment areas for these hospitals (Figure 1). The chosen suburbs, based on utilisation of sentinel sites, were mainly low-income areas within these regions.

 

Sample size: the sample size was calculated using Epi-Info 7 statistical software. It was calculated for pneumonia, the least common respiratory endpoint investigated in this study. Calculations were based on the assumption that 50% of total pneumonia cases would seek healthcare at sentinel surveillance sites [15]. This care seeking rate with a 95% confidence interval (CI) and a 10% precision yielded a minimum sample size of 96 without considering clustering effects. A cumulative incidence of 1% for pneumonia [16] required sampling of 9600 individuals to identify 96 individuals who had experienced pneumonia. Assuming an average household size of 3.5 translated into a minimum of 2743 households and an estimated 33% non-response rate gave a final sample size of 3650 households [17, 18].

 

Sampling methodology: a list of all enumerated households in the catchment areas of the two sentinel hospitals was acquired from 2011 census data [19]. Approximately 350 sub-places (suburbs with their extensions) were present in the three regions. Based on address information collected from sentinel sites, patients from 112 suburbs sought care at sentinel hospitals, but some were uncommon. Twenty-seven suburbs, most utilised by the sentinel sites were selected using purposive sampling. A list of households from suburbs of interest were randomly selected using STATA version 13 (StataCorp, College Station, Texas, USA). Mobile computing devices were used to access Google maps [20] to navigate to selected households.

 

Definitions: case definitions and recall periods for syndromes were based on previous healthcare utilisation surveys for comparability of data [6, 8, 9, 15, 21, 22]. ILI was defined as a sudden onset of self-reported fever or a measured temperature of >38ºC with cough and/or sore throat experienced in the last 30 days [9]. Pneumonia was defined as a sudden onset of self-reported fever or worsening fever >38ºC, cough and difficulty breathing that lasted 2-30 days, or diagnosed by a healthcare worker in the last year [23, 24]. Tuberculosis or chronic febrile respiratory illness (referred to as "tuberculosis") was defined as fever and cough and either difficulty breathing or weight loss that lasted ≥30 days in the last year [25]. Meningitis was defined by fever and ≥2 of the following: headache, stiff neck, confusion/altered consciousness, sensitivity to bright light or seizures/convulsions; or diagnosed by a healthcare worker, within the last year. Diarrhoea defined by ≥3 loose or watery stools within a 24 hour period within the last 14 days, was recorded for children aged <5 years. A household was a shelter in which one or more people stayed for at least one night every week excluding guests and temporary visitors. A household head or responsible person was the adult person responsible for making healthcare decisions for people in the household. Seeking healthcare was defined as presenting for treatment at any healthcare facility or provider. Registered healthcare providers were defined as those with training to diagnose respiratory illnesses and examine patients at hospitals or clinics (general practitioners, nurses), while unregistered healthcare providers were defined as those with no formal training to clinically diagnose respiratory illnesses and/or examine patients (pharmacists, religious leaders and traditional healers).

 

Data collection: dedicated, trained fieldworkers collected data using a two part structured questionnaire. The household head/responsible adult was interviewed first regarding household demographics in part one of the questionnaire after giving written consent. Part one collected information on household demographics, socioeconomic status, and identified household members who reported one of the syndromes under investigation. Part two collected individual information on specific syndromes, specific healthcare utilisation, impact and expense of the illness and deaths in the household in the last year. Part two was completed following verbal consent, either by an adult with any of the syndromes under investigation or the household head. The household head could answer individual questionnaires on behalf of other household members who experienced any of the syndromes under investigation, if the relevant individuals were not at home, if they were too young or if they indicated that the head could answer these questions on their behalf. The household head was questioned regarding any household member who died within the last year. Teams visited households up to three times on separate days and times if no adult members were home on the first visit.

 

Data analysis: survey data were captured online using REDCap software [26] and analysis was conducted using STATA version 13. Proportions were calculated for demographic data and a Fisher's exact or Chi-square test was used to determine the association of characteristics among different regions. A p-value of <0.05 was considered statistically significant. Proportions with 95% confidence intervals accounting for within household clustering were calculated by syndrome type reported and healthcare facilities attended for healthcare. We calculated the proportion of people who delayed seeking healthcare and median number of days missed from work/school. The Kruskal-Wallis test was used to determine whether there were statistically significant differences between median days missed due to respiratory syndromes.

 

Ethical considerations: to participate the household head/main respondent, aged ≥18 years, was asked to sign a consent form on behalf of the household. Approval for the main survey was obtained from the Human Research Ethics Committee of the University of the Witwatersrand (study number M120367). Permission to conduct the analysis for this paper was granted by the Faculty of Health Sciences Research Ethics Committee of the University of Pretoria (study number 549/2015).

 

 

Participants: of the total 3650 coordinates selected, 3358 were valid households and data were collected from 2930 (87%) of these households with 9850 individuals. This was above our estimated minimum sample size of 9600. The response rates in region A, 96%, (1198/1247), and C, 86% (1109/1291), were high, while only 76% (623/820) of households in region B agreed to participate. Of the households who did not complete interviews, most were due to refusal to participate (86%, 366/428) and half of these refusals (51%, 188/366) were from region B (Figure 2). Details were not collected with regards to reasons for refusal. Non-completions, due to no-one being home during any of the three visits (46/428, 11%), were mostly from region C (74%, 34/46).

 

Demographic characteristics: in the three regions, only half of the participants interviewed were heads of households, with the remainder of participants predominantly being relatives of the household head (Table 1). The racial composition of heads of households in the three regions differed significantly (p<0.001) with heads of households in region A 99% (718/725) and C 96% (526/547) being mainly black as compared to region B with more mixed race household heads, 41% (129/312). Education also differed between regions (p<0.001) with region A reporting the lowest percentage of household heads with a tertiary education, 3% (23/725), as compared to region B (28%, 87/312) and C (14%, 78/547). There was a difference in housing infrastructure by region with a higher proportion of informal structures (metal/tin or wood) in region A (69%, 824/1198) as compared to more formal houses (cement/brickstone) in region B (89%, 553/623) and C (73%, 815/1109) (p<0.001). In addition there was higher overall income and house ownership in Region B and C than Region A (Table 1). In terms of individual household members, just over half were female in all regions and median number of household members differed (p<0.001) (Table 2). HIV infection was reported for 3% (118/3403) of household members in region A, 1% (25/2375) in region B and 2% (98/4072) in region C (p<0.001). Among women of reproductive age (15-49 years old), a current pregnancy was reported in 5% (55/1146) of these women in region A, 2% (15/689) in region B and 3% (36/1238) in region C (p=0.001).

 

Frequency of reported respiratory syndromes: five percent (466/9850) of household members interviewed reported one of the respiratory syndromes (ILI, pneumonia, tuberculosis) in the defined period (Table 2) and over 90% of these interviews were completed with the person reporting the illness when they were an adult. Of individuals reporting respiratory syndromes, 92% (431/466) reported ILI symptoms within the prior 30 days, while equal numbers of participants (4%, 20/466) reported symptoms meeting pneumonia or tuberculosis case definitions within the preceding year; very few (<1%) reported more than one syndrome, i.e. either both ILI and pneumonia (3/466) or ILI and tuberculosis (2/466). The highest proportion of individuals reporting ILI was in region A (6%, 192/3403) and the lowest in region C (3%, 132/4072, p<0.001). Pneumonia and tuberculosis cases reported among all regions totalled less than 1% and there was no significant difference among regions (Table 2).

 

Frequency of other reported syndromes: there were six reported cases of meningitis (Table 2), one in a child of 14 years and the remainder in adults over 18 years of age. Most cases were from region A (4/6, 67%). Less than 1% (8/1078) of children <5 years were reported to have experienced diarrhoea in the last two weeks (Table 2) and these were predominantly from the two regions with the highest numbers of young children, region A (3/402, 0.7%) and C (4/483, 0.8%).

 

Healthcare utilisation behaviour: sixty-eight percent (295/431) of participants who reported ILI sought healthcare and two-thirds (197/295) of those who sought care utilised registered healthcare providers (Table 3). All 20 participants who reported either pneumonia or tuberculosis sought care at registered medical providers. Primary healthcare centre consultations were similar for ILI (p=0.27) between the three regions: region A 80% (56/70, 95% CI 69-89%), region B 50% (24/48, 95% CI 35-65%) and region C 62% (49/79, 95% CI 50-73%). For all syndromes, 10% (24/237, 95% CI 7-15%) of participants sought care at sentinel hospitals, with half (12/24) from region B. Among participants who reported pneumonia, 35% (7/20, 95% CI 15-59%) sought care at sentinel hospitals. Participants who reported tuberculosis mostly sought care at primary healthcare centres 80% (16/20, 95% CI 56-94%); of these same patients 31% (5/16, 95% CI 11-58%) also sought care at public hospitals. Of all participants with tuberculosis who sought care at a public hospitals (7/20), more than half (57%, 4/7) visited sentinel hospitals. Of participants who reported ILI, 38% (115/295) also sought care at unregistered healthcare providers. The most common healthcare provider that was utilised by these participants was a pharmacy. Overall, very few participants reported consulting traditional healers or religious leaders, with no regional differences (p=0.48) (Table 3). All participants with reported meningitis utilised registered healthcare providers for care (Table 3). Two participants initially went to primary healthcare clinics, and all sought care at public hospitals. Half of all participants (3/6) went to sentinel hospitals. The majority of children with diarrhoea (75%, 6/8) had some treatment given by healthcare providers. The most common source was primary healthcare clinics (67%, 4/6). Based on self-report sentinel hospitals would not have detected 93% (184/197) of ILI cases, 65% (13/20) of pneumonia cases, 80% (16/20) of tuberculosis cases, 50% (3/6) of meningitis cases and 100% (5/5) of diarrhoeal cases.

 

Delay in seeking healthcare: a delay in seeking healthcare, i.e. accessing care three or more days after symptom onset, was reported by 26% (75/295) of the participants who reported ILI, 35% (7/20) of those who reported pneumonia and 60% (12/20) of participants who reported tuberculosis. Four participants who reported meningitis and only one child with diarrhoea had a delay in accessing healthcare. Participants stated "not feeling sick enough" as the most common reason for delaying seeking healthcare for all syndromes, while worsening of symptoms and symptoms that were not getting better were the main reasons for deciding to present at healthcare facilities for care. Less than 10 patients in total reported cost, travel time or waiting time as a reason for delaying care.

 

Absenteeism due to different syndromes: one third of participants (155/466) with any respiratory syndrome reported missing school or work because of illness. The median number of days missed differed significantly between the three respiratory syndromes (p<0.05). Participants with ILI reported missing a median (range) of 2 days (1-14 days); those with pneumonia 13 days (1-30 days) and those with tuberculosis 5 days (1-30 days) per episode. Patients who reported meningitis generally missed extended periods of school/work because of illness or long hospitalisations; median of 30 days (3-60 days).

 

Mortality: a total of 42 individuals were reported to have died in the year prior to the survey; 11 (0.3%) in region A, 5 (0.2%) in region B and 26 (0.6%) in region C. Two deaths were in children <5 years of age; both had death certificates and one died in a sentinel hospital. Of the remaining deaths in older individuals, 58% (23/40) died in hospital (5 in a sentinel hospital) and a similar proportion (23/40) had death certificates. The causes listed on the death certificates that were viewed (n=24) in all age groups included, natural causes (11), unnatural causes (3), respiratory causes (4), cardiac causes (3), diabetes (1), cancer (1) and meningitis (1).

 

 

In this study, we describe health-seeking behaviour for common infectious syndromes, including three respiratory syndromes, meningitis and diarrhoea, in three regions of Johannesburg, South Africa. This study identified rates of infectious diseases similar to previous surveys [21, 22], and most reported disease was mild. We identified that health seeking behaviour differed by syndrome type and region and that severity of illness usually prompted individuals to seek healthcare. Our study showed that sentinel sites were most likely to detect sicker patients from certain areas. Overall, the two sentinel surveillance hospitals were mainly utilised by participants in regions B and C whose syndromes were severe. This could have been due to ease of access as regions B and C were in close proximity to the hospitals. Even though distance to healthcare provider was not stated as a main barrier in the study, a previous study in South Africa reported distance to healthcare facility as a barrier [27]. The catchment area was broad and not all areas were covered. This could have biased the estimated use of sentinel hospitals. ILI was the most common syndrome reported in this study at 4%. In South Africa, a typical influenza season occurs during the Southern Hemisphere's winter months (May-August) [28]. The survey was conducted at the end of the influenza season. The survey timing and shorter recall period for ILI (30 days) could have resulted in a biased estimate that underestimated ILI prevalence. A similar prevalence of ILI (4.9%) was reported in another HUS conducted from October to December 2013 in South Africa [21] with slightly higher prevalence (>6%) reported in surveys conducted in Kenya [6, 15]. As part of the ILI surveillance program in South Africa (May 2012-April 2016) the detection rate of influenza in patients with ILI was 13.9% (666/4797) [3]. Less than 1% of individuals in our study reported experiencing pneumonia or tuberculosis syndromes; these results are similar to those obtained for a South African HUS conducted in 2013 [21], but slightly lower than the 2% prevalence for pneumonia in another HUS from 2012 [22]. A lower prevalence of tuberculosis and pneumonia syndromes compared to ILI is expected as these syndromes are less common. The prevalence of reported meningitis cases was lower than reported in our previous surveys, possibly due to a more specific case definition [21, 22]. In all three regions, participants who reported ILI mainly sought healthcare at primary healthcare clinics and pharmacies. Only a few went to traditional healers and religious leaders for care. These findings are supported by a general household survey conducted by Statistics South Africa which identified primary healthcare clinics as the first point of care for mild illness and that traditional healers were not commonly consulted [18]. The majority of participants who reported pneumonia or meningitis sought healthcare at public hospitals, with most seeking healthcare at the sentinel sites. In our study, people were more likely to seek healthcare only after experiencing severe symptoms. The South African healthcare system is structured in such a way that primary healthcare clinics are the entry point for seeking care; people present at clinics with mild ailments and are only referred to hospital when their symptoms are severe. In other surveys, participants who reported pneumonia mainly sought care at hospitals [9, 15]. Another study reported hospitals as point of care for pneumonia [23] and this was linked to illness severity. Tuberculosis diagnosis and treatment in South Africa has been rolled out at primary healthcare facilities; this could explain why most people who reported tuberculosis sought care at these clinics.

 

Early disease treatment plays a role in reducing morbidity and mortality [29, 30]. Just over half of study participants indicated that not feeling sick enough was the main reason they delayed presenting to facilities for healthcare. These findings are consistent with other studies, including a national survey in South Africa, which reported not being sick enough as the main reason people delayed seeking healthcare regardless of syndrome [15, 31, 32]. On the contrary, another study conducted in rural and urban areas of South Africa stated personal finances and distance to healthcare facilities as the main reasons for delaying seeking healthcare [12, 33]. Participants in our study stated worsening symptoms as the main reason for eventually deciding to seek healthcare and this correlated with another South African study [12]. Missing school or work due to respiratory illness was frequently reported. The more severe the syndrome, the more days were missed. Being absent from school or work has negative social and economic implications. Studies conducted in high-income countries reported that absence from work/school due to severe ILI resulted in loss of productivity [34, 35]. Our study had some limitations. First, although we used standard survey case definitions, which makes our data comparable, it does not rule out the possibility of overestimating ILI because of low specificity of the case definition. Interviewers did not have medical training, which could have resulted in some misclassification between acute and chronic and mild and severe respiratory disease. Second, data collected on all syndromes were based on participant self-report and recall with no laboratory confirmation or medical record review. The recall period differed between syndromes, which may have led to differential reporting and underreporting of syndromes with a longer recall period. Other studies have shown that shorter recall periods may reduce bias [7]. Our results were not dissimilar to previous surveys which may have been subject to similar recall bias [21, 22]. Third, a proportion (12%, 58/485) of interviews were conducted with household members other than those reporting individual illness. This may have affected data completeness for individual illnesses and underreporting of underlying conditions such as HIV. In this study, the HIV prevalence was 2-5% while the estimated South African national HIV prevalence in 2012 was 12.2% [36]. The reported pregnancy rate (3.4%) was lower than that reported in the 2015 household survey (5.3%), but the latter included pregnancies in the last 12 months, while we only recorded current pregnancies [18]. Fourth, more households refused participation in region B; this may have biased the representativeness of our findings as region B had a different socio-economic profile. Region B was economically more affluent than other regions and socio-demographic, racial and economic factors have previously been associated with willingness to respond to surveys [37], which may account for the low-response rate in this region. A higher proportion of individuals from Region B accessed private healthcare and were less likely to attend sentinel hospitals for care. Individuals who feel that a survey would benefit them in some way are more likely to respond which could introduce response bias. Fifth, we did not cover all areas served by the sentinel hospitals, which may have resulted in a biased estimate of sentinel site use. Sixth, we only achieved about 20% of our target sample size for pneumonia which limited our ability to conduct sub-analyses. Last, while we made an effort to represent all residential areas, including informal settlements, it is possible that new settlements, transient populations or those living in non-residential areas, such as commercial districts, may have been systematically excluded from the sample. The strengths of this study include that households were randomly chosen using a list of all enumerated households from Statistics South Africa, the overall response rate was high and teams were monitored to ensure that completion of all syndrome information was standardised. In addition suburbs that were chosen were those identified from the surveillance program to be the source of the majority of patients attending the sentinel sites. The age structure of households was similar to that reported in the national census [19], with 11% of members <5 years and only 3% >64 years of age.

 

 

This study highlights healthcare seeking patterns within the catchment areas of a hospital-based pneumonia and laboratory-based surveillance program. Of patients with pneumonia and meningitis who sought care at public hospitals, at least half sought care at sentinel hospitals; this indicates surveillance program relevance in capturing severe disease syndromes, whereas those with milder syndromes mainly sought care at public health clinics. Using HUS data in conjunction with surveillance data will assist in estimating a more precise burden of these syndromes and aid in allocation of resources.

 

 

The authors declare no competing interest.

 

 

Relebogile Mapuroma was involved in the conception, design of the study, training of field workers, data cleaning, data analysis, interpretation of data, drafting of manuscript. Claire von Mollendorf was involved in the study conception, design, protocol writing, training, management of the field project, critical review and revision of the manuscript. Cheryl Cohen was involved the study conception and design, protocol writing and manuscript review. Alfred Musekiwa was involved in protocol writing, data analysis, critical review of the manuscript, and general biostatistical support. Lazarus Kuonza was involved in the design and revision of the manuscript and Stefano Tempia was involved in the conception and design of the study and critical review of the manuscript. Akhona Tshangela was involved in the training of fieldworkers for data collection and revision of the manuscript. All the authors have read and agreed to the final manuscript.

 

 

We thank the residents of the communities in which the survey was conducted and the fieldworkers who conducted the interviews; Dorothy L Southern for scientific writing advice and for her critical review of this manuscript and Makatisano Papo for his active contribution to the creation of the study database.

 

 

Table 1: characteristics of households, by region, Johannesburg, August to November 2015

Table 2: characteristics of individuals in households, by region, Johannesburg, August to November 2015

Table 3: registered and unregistered healthcare facilities that were reported to have been consulted by survey participants by syndrome in regions A, B and C, Johannesburg, August to November 2015

Figure 1: map showing the administrative Regions of Johannesburg and the sites where the study was conducted, August to November 2015

Figure 2: flowchart showing the number of households enrolled in the healthcare utilisation survey conducted in region A, B and C, Johannesburg, South Africa, August to November 2015

 

 

1. Statistics South Africa. Mortality and causes of death in South Africa, 2016: Findings from death notification. Accessed October 8 2018.


2. Cohen C, Moyes J, Tempia S, Groom M, Walaza S, Pretorius M et al. Severe influenza-associated respiratory infection in high HIV prevalence setting, South Africa, 2009-2011. Emerg Infect Dis. 2013 Nov; 19(11): 1766-74. PubMed | Google Scholar


3. Tempia S, Walaza S, Moyes J, Cohen AL, Von Mollendorf C, McMorrow ML et al. Attributable Fraction of Influenza Virus Detection to Mild and Severe Respiratory Illnesses in HIV-Infected and HIV-Uninfected Patients, South Africa, 2012-2016. Emerg Infect Dis. 2017 Jul; 23(7): 1124-32. PubMed


4. World Health Organization. Global Epidemiological Surveillance Standards for Influenza. Accessed October 8 2018.


5. National Institute for Communicable Diseases. Annual Overview 2015/16. Accessed July 18 2018.


6. Burton DC, Flannery B, Onyango B, Larson C, Alaii J, Zhang X et al. Healthcare-seeking behaviour for common infectious disease-related illnesses in rural Kenya: a community-based house-to-house survey. J Health Popul Nutr. 2011 Feb; 29(1): 61-70. PubMed


7. Ansah EK, Powell-Jackson T. Can we trust measures of healthcare utilization from household surveys. BMC Public Health. 2013 Sep 17; 13: 853. PubMed | Google Scholar


8. Deutscher M, Beneden CV, Burton D, Shultz A, Morgan OW, Chamany S et al. Putting surveillance data into context: the role of health care utilization surveys in understanding population burden of pneumonia in developing countries. J Epidemiol Glob Health. 2012 Jun; 2(2): 73-81. PubMed | Google Scholar


9. Lindblade KA, Johnson AJ, Arvelo W, Zhang X, Jordan HT, Reyes L et al. Low usage of government healthcare facilities for acute respiratory infections in guatemala: implications for influenza surveillance. BMC Public Health. 2011 Nov 24; 11: 885. PubMed | Google Scholar


10. Moshabela M, Schneider H, Silal SP, Cleary SM. Factors associated with patterns of plural healthcare utilization among patients taking antiretroviral therapy in rural and urban South Africa: a cross-sectional study. BMC Health Serv Res. 2012 Jul 2; 12: 182. PubMed | Google Scholar


11. Shaikh BT, Hatcher J. Health seeking behaviour and health service utilization in Pakistan: challenging the policy makers. J Public Health (Oxf). 2005 Mar; 27(1): 49-54. PubMed | Google Scholar


12. Van der Hoeven M, Kruger A, Greeff M. Differences in health care seeking behaviour between rural and urban communities in South Africa. Int J Equity Health. 2012 Jun 12; 11:31. PubMed | Google Scholar


13. Department: Statistics South Africa. Key Statistics. 2011 Accessed August 8 2018.


14. De Wet T, Patel L, Korth M, Forrester C. Johannesburg poverty and livelihoods study. 2008. Johannesburg: Centre for Social Development in Africa. Accessed August 8 2018.


15. Breiman RF, Olack B, Shultz A, Roder S, Kimani K, Feikin DR et al. Healthcare-use for major infectious disease syndromes in an informal settlement in Nairobi, Kenya. J Health Popul Nutr. 2011 Apr; 29(2): 123-33. PubMed


16. Cohen C, Walaza S, Moyes J, Groome M, Tempia S, Pretorius M et al. Epidemiology of severe acute respiratory illness (SARI) among adults and children aged >/=5 years in a high HIV-prevalence setting, 2009-2012. PLoS One. 2015; 10(2): e0117716. PubMed | Google Scholar


17. Housing Development Agency South Africa. Informal settlements Status 2013. Accessed October 20 2017.


18. Statistics South Africa. General household survey. Statistical release P03 2015. Accessed October 20 2017.


19. Statistics South Africa. Census 2011: Statistical release. P0301.4. 2012. Accessed October 28 2017.


20. Google maps. Google maps 2015. Accessed October 28 2017.


21. McAnerney JM, Cohen C, Cohen A, Tempia S, Walaza S, Wong KK et al. Healthcare utilization patterns for common syndromes in Msunduzi Municipality, Pietermaritzburg, KwaZulu-Natal Province, South Africa, 2013. S Afr Med J. (In press) 2019.


22. Wong KK, von Mollendorf C, Martinson N, Norris S, Tempia S, Walaza S et al. Healthcare utilization for common infectious disease syndromes in Soweto and Klerksdorp, South Africa. Pan Afr Med J. 2018; 30: 271. PubMed | Google Scholar


23. Jordan HT, Prapasiri P, Areerat P, Anand S, Clague B, Sutthirattana S et al. A comparison of population-based pneumonia surveillance and health-seeking behavior in two provinces in rural Thailand. Int J Infect Dis. 2009 May; 13(3): 355-61. PubMed | Google Scholar


24. Olsen SJ, Laosiritaworn Y, Siasiriwattana S, Chunsuttiwat S, Dowell SF. The incidence of pneumonia in rural Thailand. Int J Infect Dis. 2006 Nov; 10(6): 439-45. PubMed | Google Scholar


25. Reid MJ, Shah NS. Approaches to tuberculosis screening and diagnosis in people with HIV in resource-limited settings. Lancet Infect Dis. 2009 Mar; 9(3): 173-84. PubMed | Google Scholar


26. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr; 42(2): 377-81. PubMed | Google Scholar


27. McLaren ZM, Ardington C, Leibbrandt M. Distance decay and persistent health care disparities in South Africa. BMC Health Serv Res. 2014 Nov 4; 14: 541. PubMed | Google Scholar


28. McAnerney JM, Cohen C, Moyes J, Besselaar TG, Buys A, Schoub BD et al. Twenty-five years of outpatient influenza surveillance in South Africa, 1984-2008. J Infect Dis. 2012 Dec 15; 206 Suppl 1: S153-8. PubMed | Google Scholar


29. Krumkamp R, Sarpong N, Kreuels B, Ehlkes L, Loag W, Schwarz NG et al. Health care utilization and symptom severity in Ghanaian children--a cross-sectional study. PLoS One. 2013; 8(11): e80598. PubMed | Google Scholar


30. Wardlaw T, Salama P, Johansson EW, Mason E. Pneumonia: the leading killer of children. Lancet. 2006 Sep 23; 368(9541): 1048-50. PubMed | Google Scholar


31. Duan QH, Wang P, Lv MX, Zeng QZ. Patterns of health care seeking behavior among the suspects of pulmonary tuberculosis. Zhonghua Liu Xing Bing Xue Za Zhi. 2013 Jun; 34(6): 650-3. PubMed | Google Scholar


32. Statistics South Africa. Use of health facilities and levels of selected health conditions in South Africa: Findings from the General Household Survey, 2011. Accessed October 20 2017.


33. Noordam AC, Carvajal-Velez L, Sharkey AB, Young M, Cals JW. Care seeking behaviour for children with suspected pneumonia in countries in sub-Saharan Africa with high pneumonia mortality. PLoS One. 2015; 10(2): e0117919. PubMed | Google Scholar


34. Chow MY, Morrow AM, Booy R, Leask J. Impact of children's influenza-like illnesses on parental quality of life: a qualitative study. J Paediatr Child Health. 2013 Aug; 49(8): 664-70. PubMed | Google Scholar


35. Palmer LA, Rousculp MD, Johnston SS, Mahadevia PJ, Nichol KL. Effect of influenza-like illness and other wintertime respiratory illnesses on worker productivity: The child and household influenza-illness and employee function (CHIEF) study. Vaccine. 2010 Jul 12; 28(31): 5049-56. PubMed | Google Scholar


36. Shisana O, Rhele T, Simbayi LC, Zuma K, Jooste S, Zungu N et al. South African National HIV Prevalence, Incidence and Behaviour Survey, 2012. HRSC Cape Town 2014.


37. Connolly C, Shisana O, Colvin M, Stoker D. Epidemiology of HIV in South Africa--results of a national, community-based survey. S Afr Med J. 2004 Sep; 94(9): 776-81. PubMed | Google Scholar