Introduction
Methods
Category | Search terms (in Pubmed database) |
---|---|
ART
| Antiretroviral therapy, highly active [MeSH Terms] OR ART [title/abstract] OR HAART [title/abstract] OR AR V [title/abstract] OR ARVs [title/abstract] OR Anti-Retroviral Agents [Mesh] OR antiretroviral [title/abstract] OR anti retroviral [title/abstract] OR anti-retroviral [title/abstract] OR antiviral [title/abstract] OR therapy [title/abstract] |
AND | |
HIV
| Acquired immunodeficiency syndrome [MeSH Terms] OR acquired immunodeficiency syndrome [title/abstract] OR aids [title/abstract] OR hiv [MeSH Terms] OR hiv [title/abstract] OR human immunodeficiency virus [title/abstract] OR HIV infections [MeSH Terms] |
AND | |
South Africa
| (South Africa [MeSH Terms] OR (South [title/abstract] AND Africa* [title/abstract])) |
AND | |
Equity
| (Equity [title/abstract] OR equities [title/abstract] OR inequity [title/abstract] OR inequities [title/abstract] OR equality [title/abstract] OR equalities [title/abstract] OR equal [title/abstract] OR equitable [title/abstract] OR inequality [title/abstract] OR inequalities [title/abstract] OR unequal [title/abstract] OR disparity [title/abstract] OR disparities [title/abstract] OR vulnerability [title/abstract] OR fairness [title/abstract] OR unfair [title/abstract] OR social justice [MeSH Terms] OR social justice [title/abstract] OR justice [title/abstract] OR barrier [title/abstract] OR coverage [title/abstract] OR barriers [title/abstract] OR healthcare disparities [MeSH Terms] OR health services accessibility [MeSH Terms] OR health services accessibility [title/abstract] OR access to health care [title/abstract]) |
Conceptual model
Inclusion and exclusion criteria
Study selection, data extraction and quality evaluation
Data synthesis and analysis
Results
Study inclusion
Reference |
Equity criteria
| |||||||
---|---|---|---|---|---|---|---|---|
Name, year | Quality score | Sex | Age | Severity of disease | Area of living | Socio-economic status (including education and employment) | Marital status | Ethnicity |
ART initiation
(lower < higher likelihood to initiate)
| ||||||||
Cleary 2011[25] | *** | Not associated, men = women | SES: not associated, SES distribution HIV + in need = ART clinic patients | Inconclusive results population size too small to draw conclusions | ||||
Cooke 2010[32] | *** |
Not associated
| ||||||
men = women | Associated, younger (15–19 yrs), < older (>19 yrs) | Not associated, peri-urban = urban = rural | SES: not associated, index profile 1 = 2 = 3 = 4 = 5 (SES), education: not associated, years of education as continuous variable | |||||
Govindasamy 2011[26] | *** | Not associated, men = women | Not associated, ≤ 30 yrs = ≥ 30 yrs | Contradicting results, CD4 cell count: associated, CD4 > 350 < ≤350, WHO stages: not associated | Education: not associated, primary school completed = not completed, employment: not associated, employed < unemployed | |||
Tsai 2009[33] | ** | Not associated, men = women | Associated, younger (18–30 yrs) < older (30–35 yrs) | Education: associated, lower education (secondary) < higher education (matric/ tertiary), employment: associated, non salaried employment < salaried employment, unemployed < employed | Associated never married < married or cohabiting | |||
Adam 2009[34] | ** | Associated
4
, unequal ART coverage between 9 provinces | ||||||
Muula 2007[24] | * | Associated
4
, male < female | ||||||
Nattrass 2006[23] | Associated
4
, unequal ART coverage between 9 provinces | |||||||
ART adherence
(poorer < better adherence)
| ||||||||
Boyles 2011[30] | *** | Not associated, men = women | Associated, younger (<25 yrs) < older, (25–50 yrs) | Associated, CD4 ≥ 200 < <200 | ||||
Orrell 2003[31] | *** | Not associated, men = women | Associated, younger < older, adherence group is older (34 yrs) than non-adhererce group (31 yrs) | Contradicting results, CD4 cell count: associated, patients that not adhere had lower CD4 level, Viral load: associated, patients that not adhere had higher VL, WHO stage: not associated | SES: not associated, % low SES in patient group that continued, ART = that not continued | |||
Kranzer 2010[27] | *** | Associated, men < women | Not associated, ≤ 30 yrs = > 30 yrs | Contradicting results, CD4 cell count: associated, > 200 < ≤100, WHO stage: not associated | ||||
Fatti 2010[29] |
***
| Not associated, men = women | Not associated, younger children (≤2 yrs) = older children (>2 yrs) | Contradicting results, CD4 cell count (severe immunodeficiency
1
): associated, patients with severe immunodeficiency were less adherent, WHO stage (severe clinical status
2
): not associated | Associated, rural/urban < urban/urban < rural/ rural3 | |||
Cornell 2009[28] | ** | Not associated, men = women | Not associated, age as continuous variable | Contradicting results, CD4 cell count: associated, CD4 < 50 < 50–150, but CD4 < 50 = >150, WHO stage: not associated, Viral load: not associated | Employment: associated, no income < income |
Characteristics of included studies
Study, quality score, study type | Study objective | Study area, type of clinic/program | Year of data collection | Study design (comparison between population a and b), population sizes, sampling method/inclusion criteria | Statistical analysis | Outcome on association as reported per equity criteria |
---|---|---|---|---|---|---|
Cleary 2011 *** Observational [25] | To evaluate whether the distribution of ART services in the public system reflects the distribution of people in need among adults in the urban population | Urban area: poor communities in Mitchells Plain (Cape Town, Western Cape province) and Soweto township (Johannesburg, Gauteng province), public clinics | National survey: 2008. Urban clinic data: unknown | a. Population in need for ART (n = 742): national survey (2008, HIV + residents), sampling unknown, | Comparison distribution of equity criteria (i.e. patients characteristics) | Sex (not associated): percentage of HIV + women in national survey is same as in ART users in urban clinic; 67.4% [95% CI: 61.5-72.9] versus 65.7% [95% CI: 60.6-70.7], p >0.05. Socioeconomic status (not associated): no significant differences in SES distribution between HIV + in need for ART and ART patients in urban clinics; independence partition Pearson’s chi-square test: 8 [p = 0.43] Race/ethnicity (inconclusive results): percentage of non-African is 2,5% in population HIV + in need versus 4.3% of ART users in urban clinics, authors state that sample size of non-African is too small to draw conclusions on equity |
b. ART patients in urban public clinics (n = 635): data from ART users (>18 yrs, >14 days on treatment) in three clinics in Mitchells Plain (selected proportional to the number of ART patients in facility) and three in Soweto (stratified random sampling) | ||||||
Cooke 2010 *** Observational [32] | To investigate factors associated with uptake of ART through a primary health care system in rural South Africa | Rural, peri-urban and urban areas: Hlabisa sub-district, Umkhanyakude district, Northern KwaZulu-Natal province, public clinics supported by NGOs | Aug 2004 – Dec 2008 | a. HIV + residents not on ART (n = 1,003): population-based surveillance in 6 catchment areas, | Multivariate logistic regression | Sex (not associated): no significant association between gender and receiving treatment: aOR men 0.875 [95% CI: 0.708-1.081, p = 0.216] Age (associated, younger (15–19 yrs) < older (>19 yrs)): compared to age 15–19 (reference) all higher 5-year-age-groups [20–24, 25–29, 30–34, 35–40, 40–45, 45–50, 50–54, 55–60, >60] have significant higher aOR [ranging between 4.9-14.0, p < 0.05] for receiving treatment Area of living (not associated): no significant differences in aORs between peri-urban [1.042, 95% CI: 0.699-1.554, p = 0.838], rural [0.941, 95% CI: 0.628-1.410, p = 0.768] and urban (reference) areas for receiving treatment Socioeconomic status (not associated): no significant differences in aORs between index profiles 1 (reference), 2 [0.932, 95% CI: 0.688-1.262, p = 0.649], 3 [0.842, 95% CI: 0.624-1.135, p = 0.258], 4 [0.829, 95% CI: 0.607-1.131, p = 0.237] and 5 [0.984, 95% CI: 0.702-1.379, p = 0.927] for receiving treatment Education (not associated): no significant association between years of education and receiving treatment; aOR years of education: 1.022 [95% CI: 0.995-1.063, p = 0.128] |
b. HIV + residents on ART (n = 1,251): population based 2008 cohort (HIV+, > 15 yrs, on ART) | ||||||
Govindasamy 2011 *** Observational [26] | To assess the proportion and characteristics of individuals who accessed HIV care after testing HIV + in a mobile testing unit | Rural area: Cape Metropolitan region, Western Cape province, type of clinic not clearly reported | Tested HIV+: 2008–2009. Interviewed: Apr-Jun 2010. | Patients tested HIV + in mobile testing units that: a. linked to ART care (i.e. receiving CD4 test result), b. not linked, A random sample of patients tested HIV + between August 2008 – December 2009, ≥18 yrs, CD4 < 350, received CD4 test results, available socio-demographic variables was selected using mobile testing unit records (n = 77) | Binomial univariate and bivariate regression analysis | Sex (not associated): same likelihood to link to care for female as male patients; **RR female: 1.18 [95% CI: 0.81-1.72, p = not reported, 1.0 falls within CI] Age (not associated): same likelihood to link to care for younger (≤30 years) as older patients (≥30 years) to link to care; **RR ≥30 years: 1.21 [95% CI: 0.83-1.77, p = not reported, 1.0 falls within CI] Severity of disease (contradicting results, CD4 cell count associated and WHO stages not associated): significantly lower likelihood to link to care for patients with high (>350) compared to low (≤350) CD4 cell count;*RR CD4 > 350: 0.49 [95% CI: 0.27-0.87, p = 0.014] / same likelihood to link to care for patients in WHO stage I as WHO stage II, III or IV; **RR WHO clinical stage I: 0.88 [95% CI: 0.65-1.18, p = not reported, 1.0 in CI] Education (not associated): same likelihood to link to care for patient completed primary school as patients that have not; **RR completed primary school: 1.17 [95% CI: 0.66-2.08, p = not reported, 1.0 falls within CI] Employment (not associated, employed < unemployed): likely lower likelihood to link to care for employed compared to unemployed patients; **RR employed: 0.72 [95% CI: 0.51-1.01, p = 0.056]. * = univariate ** = bivariate analysis |
Tsai 2009 ** Observational [33] | To assess differences in socioeconomic profiles between those who access HIV-related clinical services and the HIV-infected individuals living in the wider community | Rural area: Limpopo province, public hospital | Community survey: 2004-2005. Clinic survey: Jan 2003 – Nov 2005 | a. community sample, HIV + not on ART (n = 242): household survey, random sampled from eight rural villages in the province (14–35 yrs, HIV+), | Uni-variate comparison and multiple regression | Sex (not associated): no significant difference percentage women in the community vs. clinic sample: 79% vs. 79% [p = 0.78] Age (associated, younger (18–30 yrs) < older (30–35 yrs)): significant difference in age distribution between community and clinic sample: 18–20 yrs: 13% vs 3.6%; 21–25 yrs: 33% vs. 16%; 26–30 yrs: 36% vs 33%; 31–35 yrs: 18% vs. 47%; X2 = 85 [p < 0.001*] Education (associated, higher education > lower education): significant difference in distribution educational attainment between community and clinic sample: in clinic less likely to completed secondary education [p < 0.001], but more likely to completed matric or tertiary education [p = 0.04] X2 42 [p < 0.001*] Employment (associated, not having salaried employment < having salaried employment, unemployed < employed): significant difference percentage having salaried employment between community and clinic sample: 6.2% vs. 11%, X2 3.8 [p = 0.05] and in percentage unemployed and able to work: 57% vs. 37%; X2 26 [p < 0.001*] Marital status (associated, never married < married or cohabiting): significant difference distribution marital status between community and clinic sample: never married: 78% vs. 43%; married/ cohabiting: 16% vs. 30%; X2 83 [p < 0.001*] *also significant after multivariable regression |
b. clinical sample, HIV + on ART (n = 534): convenience sample of patients (18–35 yrs) in primary HIV/AIDS provider hospital, referred by 45 primary health care clinics. Note: samples were not taken from identical sub-districts | ||||||
Adam 2009 ** Observational [34] | To quantify the coverage in South Africa up to the middle of 2008, according to various definitions of antiretroviral treatment eligibility | Rural and urban: National/ nine provinces, public clinics | 2008 | For nine provinces: a. number of HIV + in need for ART: Markov model on HIV progression using different CD4 count compartments | Comparison ART coverage data | Area of living (associated, unequal coverage among nine provinces): unequal ART coverage in 2008 among 9 provinces: Eastern Cape 32.4%, Free State 25.8%, Gauteng 43.5%, KwaZulu-Natal 39.4%, Limpopo 32.2%, Mpumalanga 31.2%, Northern Cape 61.1%, North West 35.4%, Western Cape 71.1% |
b. number of HIV + on ART: estimates of patients starting ART in public health facilities using Department of Health unpublished internal report (7 May 2009) | ||||||
Muula 2007 * Systematic review [24] | To describe the gender distribution of patients accessing ART in Southern Africa | Rural and urban: National (1999–2004), Khayelisha township in Capetown (2001–2), Eastern cape province 2001–4), Northern cape province (2001–5), public clinics | 2000 – 2006 |
a. National HIV + prevalence female/male ratio in 2005,
| Comparison female/male ratios | Sex (associated, male < female): female have higher access than men to ART: HIV prevalence female/male ratio = 1.2, while 4 studies report access to ART female/male ratio of 1.9, 2.3, 1.8 and 1.5 |
b. access to ART female/male ratio. Sampling methods not reported | ||||||
Nattrass 2006 * Critical assessment [23] | To compare ART roll-out in public sector between provinces in 2003-2005 | Rural and urban: National (nine provinces), public clinics | 2003 - 2005 | For nine provinces: | Comparison ART coverage data | Area of living (associated, unequal coverage among 9 provinces): unequal ART coverage at the end of 2005 among 9 provinces: Eastern Cape 21.8%, Free State 21.0%, Gauteng 29.6%, KwaZulu-Natal 20.0%, Limpopo 27.3%, Mpumalanga 20.9%, Northern Cape 32.3%, North West 24.5%, Western Cape 55.7% |
a. number of HIV + in need for ART,
| ||||||
b. number of HIV + on ART, estimates of ART coverage based on ASSA2003 demographic model (includes public, NGOs and private sector providers) |
Study, quality score, study type | Study objective | Study area, type of clinic/program | Year of data collection | Study design (comparison between population a and b), population sizes, sampling method and inclusion criteria | Statistical analysis | Main outcome of analyzed equity criteria |
---|---|---|---|---|---|---|
Boyles 2011 *** Observational [30] | To determine the factors predicting loss to follow-up and mortality in a public-sector HIV and ART programme in rural South Africa | Rural area: Elliotdale/Xora area of Mbhashe sub-district in Eastern Cape province, combined public/donor program | Jan 2005 – Sept 2009 |
a. HIV + patients that loss to follow up (n = 117 (6.5%)),
| Multiple Cox proportional hazard regression | Sex (not associated): females and males have same risk of being loss-to-follow-up: HR female: 1.42 [95% CI 0.90-2.23, p = 0.134] Age (associated, younger (<25 yrs) < older (25–50 yrs)): younger people have significant higher risk to loss-to-follow-up: HR <25 yrs (compared to 25–50 yrs): 1.87 [95% CI: 1.15-3.05, p = 0.012] Severity of disease (associated, ≥ 200 CD4 < <200 CD4): higher CD4 cell count significantly increases risk to loss-to-follow-up: 50–199 CD4 (referent); HR 0–49 CD4: 1.00 [95% CI: 0.61-1.64, p = 0.019]; HR ≥ 200 CD4: 1.74 [95% CI 1.09-2.78, p = 0.019] |
b. HIV + patients that do not loss to follow up (n = 1686). Both groups are patients enrolled in clinics of Madwaleni HIV wellness and ART program including adherence counseling and home visits (i.e. Madwaleni Hospital, its 7 primary healthcare feeder clinics and a community based outreach program): tested HIV+, ART naïve at time of study enrollment, >19 years, initiated ART (CD4 < 200 CD4), could be follow for at least 3 months (n = 1803) | ||||||
Orrell 2003 *** Observational [31] | To determine adherence of an indigent African HIV-infected cohort initiating ART to identify predictors of incomplete adherence and virologic failure | Urban area: Cape Town, Western Cape province, university of Cape Town clinic | Jan 1996 – May 2001 |
a. Patients discontinued 48 weeks of ART (n = 47),
| T-test (age, VL, CD4 cell count), X2 test (gender, socioeconomic status) | Sex (not associated): no significant difference in percentage female between those discontinued (40.4%) and completed (43.4%) 48 weeks of ART [p = 0.7] Age (associated, younger < older): those discontinued ART before 48 weeks were significantly younger (31 yrs) than those completed (34.1 yrs) [p <0.005] Severity of disease (contradicting results, CD4 cell count associated and WHO stages not associated): those discontinued ART before 48 weeks had significantly lower mean CD4 cell count (197) than those completed (268) [p < 0.01] / those discontinued before 48 weeks ART had a significantly higher VL (5.71 log10) than those completed (5.49 log10) [p <0.05] / no significant difference in percentage WHO stage 3 or 4 between those discontinued (49.2%) and completed (38.2%) 48 weeks of ART [p = 0.2] Socio-economic status (not associated): no significant difference in the percentage of patients with low socio-economic status in the group that discontinued (36.2%) and completed (43.6%) 48 weeks of ART [p = 0.4] |
b. Patients that completed 48 weeks of ART (n = 242). Both groups are from Cape Town AIDS Cohort (CTAC): HIV + patients, presenting at University of Cape Town HIV clinics (referred by health care workers in the public sector of the wider Cape town area, mainly serving indigent populations), were ART naïve and eligible for adherence monitoring | ||||||
Kranzer 2010 *** Observational [27] | To investigate the frequency and risk factors of defaulting treatment and identify factors associated with subsequent return to care in a long-term treatment cohort in South Africa | Peri-urban: township in Cape Town, Western Cape province, public clinic | Mar 2004 - Dec 2009 |
a. HIV + patients that defaulted ART (n = 291),
| Multivariate Poisson regression | Sex (associated, men < women): compared to women, men have a significant increased risk to default ART treatment, HR men: 1.51 [95% CI: 1.18-1.93, p < 0.01] Age (not associated): no significant association between age and defaulting treatment, compared to younger age (≤30 years), HR > 30 years: 0.90 [95% CI: 0.70-1.15, p = 0.40] Severity of disease (contradicting results, CD4 cell count associated and WHO stages not associated): higher CD4 cell count increases significantly risk for defaulting treatment, ≤100 CD4 (referent); 101–200 CD4: HR 1.32 [95% CI: 0.99-1.76, p = 0.06], CD4 > 200 HR: 1.39 [95% CI 1.02-1.91, p = 0.04]. No significant difference in the risk of defaulting treatment being in WHO stage 3/4 or 1/2, HR stage 3/4: 1.14 [95% CI: 0.85-1.53, p = 0.37] |
b. HIV + patients that not defaulted ART (n = 863). Both groups are from patients presenting at public-sector primary care clinic (single ART server in the area), >15 years, started ART (until 2007 < 350 CD4 cells (NIH research study), after 2007 < 200 CD4 cells (provincial ART program) (n = 1154) | ||||||
Fatti 2010 *** Retrospective cohort study [29] | To compare clinical, immunological and virological outcomes between rural and urban children on ART in a large cohort from multiple public health facilities in four provinces of South Africa | Rural and urban: areas in Western Cape, KwaZulu-Natal, Eastern Cape and Mpumalanga province, public clinics supported by NGOs | Nov 2003 – Mar 2008 |
a. Children on ART that loss to follow up (n = 179),
| Multivariable Cox proportional hazards regression | Sex (not associated): gender is not associated with risk of LTFU: HR male: 1.1 [95% CI: 0.82-3.12, no p value reported, 1.0 falls within CI] Age (not associated): younger children (<2 yrs) are as likely to LTFU than older children (>2 yrs): > 2 yrs (referent); HR 1–2 yrs: 1.61 [95% CI: 0.96-2.68, no p value reported, 1.0 in CI > 0.90]; HR < 1 yr: [1.81, 95% CI: 0.94-3.64, no p value reported, 1.0 in CI] Severity of disease (contradicting results, CD4 cell count not associated and WHO stages associated): severe clinical status is associated with risk LTFU: HR severe clinical status: 1.47 [95% CI: 1.03-2.12, no p value reported, 1.0 not within CI]/ severe immunodeficiency was associated with risk LTFU: HR severe immunodeficiency: 0.81 [95% CI: 0.52-1.24, p value not reported, 1.0 in CI Area of living (associated, rural/urban < urban/urban < rural/rural): patient in rural areas visiting clinics in urban areas are more likely to LTFU than patients from rural areas visiting rural clinics and patients in urban areas visiting urban clinics: rural (referent); HR urban: 1.14 [95% CI: 0.57-2.24]; HR rural/urban 2.85 [95% CI, 1.41-5.79] [p = 0.004] |
b. Children on ART that do not loss to follow up (n = 2153). Both from retrospective cohort of children, (<16 yrs, ART naïve), enrolled in 44 routine public healthcare facilities (7 rural, 33 urban/12 secondary level hospitals, 32 primary health care clinics) supported by a NGO, used electronic data collection systems for patient monitoring. Children were divided in 3 groups a) urban residence and urban ART facility attended (urban group, n = 1727); rural residence and rural facility attended (rural group, n = 228); and rural residents attending urban facilities (rural/urban group, n = 377) | ||||||
Cornell 2009 ** Observational [28] | To investigate the impact of gender and income on survival and retention in a South African public sector ART programme | Urban: Nyanga township, outskirts of Cape Town, Western Cape province, public clinics supported by NGOs | Sept 2002 – Apr 2007 |
a. HIV + patients that loss to follow up (n = 137),
| Proportional hazards regression models | Sex (not associated): gender is not associated with risk to LTFU: HR men: 1.38, [95% CI: 0.94-2.03, p = 0.100] Age (not associated): no significant difference between age and risk to LTFU: HR age: 0.98 [95% CI 0.96-1.00, p = 0.102] Severity of disease (contradicting results, CD4 cell count associated and WHO stages not associated): patients with CD4 cell count <50 have higher risk to LTFU than CD4 cell count 50–150, but a similar risk as CD4 > 150: CD4 < 50 (referent); HR CD4 51–100: 0.62 [95% CI: 0.37-1.05, p = 0.077]; HR CD4 101–150 [0.57, 95% CI: 0.33-1.00, p = 0.049]; HR CD4 > 150: 1.01 [95% CI: 0.64-1.59, p = 0.971]/ WHO stage has no association with risk to LTFU: WHO stage I & II (referent); HR stage III: 0.78 [95% CI: 0.50-1.21, p = 0.274] HR stage IV: 0.75 [95% CI 0.75 (0.44-1.28), p = 0.294] /VL was not significantly associated with risk to LTFU: HR RNA level <5 log10 copies/ml (referent); >5 log: 1.13 [95% CI: 0.78–1.64, p = 0.520] Employment (associated, no income < income): patient with no income have a increased risk to LTFU: HR with income: 0.53 [95% CI: 0.37-0.77, p = 0.002] |
b. HIV + patients that do not loss to follow up (n = 2059). Both groups from Gugulethu clinic patient cohort that receive adherence counseling including home visits, >15 years, ART naïve, WHO stage IV or CD4 < 200 (n = 2196) |
High (30–40 points) | Medium (20–29 points) | Low (<20 points) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Studies | |||||||||||||
Kranzer[[27]] | Cooke[[32]] | Fatti[[29]] | Govindasamy[[26]] | Boyles[[30]] | Cleary[[25]] | Orrell,[[31]] | Cornell[[28]] | Tsai[[33]] | Adam[[34]] | Muula[[24]] | Nattrass[[23]] | ||
Total score (out of 40 points) |
37
|
34
|
34
|
33
|
32
|
31
|
30
|
26
|
26
|
26
|
19
|
12
| |
1 | Study design (peer reviewed = 2, other = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
2 | Well-defined hypothesis/objective/research question? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 |
3 | Clear motivation research question? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
4 | Concept clearly defined (e.g. access, equity) (fully = 2, partial = 1, not at all = 0) | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 0 | 1 | 2 | 0 | 0 |
5 | Methods well described? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 0 |
6 | Main outcomes clearly described? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 1 |
7 | Potential sources of bias taken into account? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 0 | 0 | 2 | 0 | 0 |
8 | Population and sampling method clearly defined? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 0 | 0 | 0 |
9 | Type of information used (i.e. sample size, time period) clearly described? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 1 |
10 | Primary data used for key analyses? (yes = 2, no = 0) | 2 | 0 | 2 | 2 | 2 | 0 | 2 | 2 | 0 | 0 | 0 | 0 |
11 | Survey (household/provider level) data used? (yes = 2, partial = 1, no = 0) | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 0 | 2 | 0 | 0 |
12 | Research/subquestion(s) answered? (fully = 2, partial = 1, not at all = 0) | 1 | 2 | 2 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 1 |
13 | Results based on evidence derived from the data analysis? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 0 | 0 | 1 |
14 | Results credible given the methods, data, and analysis used? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 0 | 1 |
15 | Robustness of findings and limitations of method discussed? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 0 |
16 | Findings discuss within context of existing evidence base? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
17 | Missings clearly described? (fully = 2, partial = 1, not at all = 0) | 2 | 2 | 1 | 0 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 0 |
18 | Generalizable to rest of the country? (given sample size) (fully = 2, partial = 1, not at all = 0) | 2 | 1 | 2 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 2 | 2 |
19 | Study subjects asked representative of entire population recruited from? (yes = 2, no = 0) | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 |
20 | Study subjects prepared to participate representative of entire population recruited from? (yes = 2, partial = 1, no = 0) | 2 | 2 | 0 | 2 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 0 |