This article provides the first ever review of literature analysing the health policy processes of low and middle income countries (LMICs). Based on a systematic search of published literature using two leading international databases, the article maps the terrain of work published between 1994 and 2007, in terms of policy topics, lines of inquiry and geographical base, as well as critically evaluating its strengths and weaknesses. The overall objective of the review is to provide a platform for the further development of this field of work.From an initial set of several thousand articles, only 391 were identified as relevant to the focus of inquiry. Of these, 164 were selected for detailed review because they present empirical analyses of health policy change processes within LMIC settings. Examination of these articles clearly shows that LMIC health policy analysis is still in its infancy. There are only small numbers of such analyses, whilst the diversity of policy areas, topics and analytical issues that have been addressed across a large number of country settings results in a limited depth of coverage within this body of work. In addition, the majority of articles are largely descriptive in nature, limiting understanding of policy change processes within or across countries. Nonetheless, the broad features of experience that can be identified from these articles clearly confirm the importance of integrating concern for politics, process and power into the study of health policy. By generating understanding of the factors influencing the experience and results of policy change, such analysis can inform action to strengthen future policy development and implementation. This article, finally, outlines five key actions needed to strengthen the field of health policy analysis within LMICs, including capacity development and efforts to generate systematic and coherent bodies of work underpinned by both the intent to undertake rigorous analytical work and concern to support policy change.

Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15-49 years and a tuberculosis prevalence of 0•7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. MethodsIn this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. FindingsAmong the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23•3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37•4%) of 163 350, diabetes in 43 885 (27•4%) of 159 932, and HIV in 13 793 (9•1%) of 151 779. Tuberculosis was reported in 5282 (3•6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1•34, 95% CI 1•27-1•43), past tuberculosis (1•26, 1•15-1•38), current tuberculosis (1•42, 1•22-1•64), and both past and current tuberculosis (1•48, 1•32-1•67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1•45, 95% CI 1•22-1•72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29•2% compared with 30•8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with...

Background The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.Methods In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.Findings Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240•4 cases per 100 000 people vs 136•0 cases per 100 000 people; admissions, 27•9 admissions per 100 000 people vs 16•1 admissions per 100 000 people; deaths, 8•3 deaths per 100 000 people vs 3•6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1•19, 95% CI 1•18-1•20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1•22, 95% CI 1•14-1•31), and older than 65 years (aOR 1•38, 1•25-1•52), compared with younger than 40 years; of Mixed race (aOR 1•21, 1•06-1•38) compared with White race; and admitted in the public sector (aOR 1•65, 1•41-1•92); and less likely to be Black (aOR 0•53, 0•47-0•60) and Indian (aOR 0•77, 0•66-0•91), compared with White; and have a comorbid condition (aOR 0•60, 0•55-0•67).For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1•31, 95% CI 1•28-1•35). In-hospital case-fatality risk increased from 17•7% in weeks of low admission (<3500 admissions) to 26•9% in weeks of very high admission (>8000 admissions; aOR 1•24, 1•17-1•32).Interpretation In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.

BackgroundThe World Health Organization recommends that antiretroviral therapy be started as soon as possible, irrespective of stage of HIV infection. This ‘test and treat’ approach highlights the need to ensure that men are involved in prevention of mother-to-child HIV transmission (PMTCT). This article presents findings from a rapid appraisal of strategies to increase male partner involvement in PMTCT services in Uganda, Democratic Republic of Congo, Malawi, and Côte d'Ivoire in the context of scale-up of Option B+ protocol.DesignData were collected through qualitative rapid appraisal using focus groups and individual interviews during field visits to the four countries. Interviews were conducted in the capital city with Ministry of Health staff and implementing partners (IPs) and at district level with district management teams, facility-based health workers and community health cadres in each country.ResultsCommon strategies were adopted across the countries to effect social change and engender greater participation of men in maternal, child and women's health, and PMTCT services. Community-based strategies included engagement of community leaders through dialogue and social mobilization, involving community health workers and the creation and strengthening of male peer cadres. Facility-based strategies included provision of incentives such as shorter waiting time, facilitating access for men by altering clinic hours, and creation of family support groups.ConclusionsThe approaches implemented at both community and facility levels were tailored to the local context, taking into account cultural norms and geographic regional variations. Although intentions behind such strategies aim to have positive impacts on families, unintended negative consequences do occur, and these need to be addressed and strategies adapted.A consistent definition of ‘male involvement’ in PMTCT services and a framework of indicators would be helpful to capture the impact of strategies on cultural and behavioral shifts. National policies around male involvement would be beneficial to streamline approaches across IPs and ensure wide-scale implementation, to achieve significant improvements in family health outcomes.

BackgroundIn October 2012 Uganda extended its prevention of mother to child HIV transmission (PMTCT) policy to Option B+, providing lifelong antiretroviral treatment for HIV positive pregnant and breastfeeding women. The rapid changes and adoptions of new PMTCT policies have not been accompanied by health systems research to explore health system preparedness to implement such programmes. The implementation of Option B+ provides many lessons which can inform the shift to ‘Universal Test and Treat’, a policy which many sub-Saharan African countries are preparing to adopt, despite fragile health systems.MethodsThis qualitative study of PMTCT Option B+ implementation in Uganda three years following the policy adoption, uses the health system dynamics framework to explore the impacts of this programme on ten elements of the health system. Qualitative data were gathered through rapid appraisal during in-country field work. Key informant interviews and focus group discussions (FGDs) were undertaken with the Ministry of Health, implementing partners, multilateral agencies, district management teams, facility-based health workers and community cadres. A total of 82 individual interviews and 16 focus group discussions were completed. We conducted a simple manifest analysis, using the ten elements of a health system for grouping data into categories and themes.ResultsOf the ten elements in the health system dynamics framework, context and resources (finances, infrastructure & supplies, and human resources) were the most influential in the implementation of Option B+ in Uganda. Support from international actors and implementing partners attempted to strengthen resources at district level, but had unintended consequences of creating dependence and uncertainty regarding sustainability.ConclusionsThe health system dynamics framework offers a novel approach to analysis of the effects of implementation of a new policy on critical elements of the health system. Its emphasis on relationships between system elements, population and context is helpful in unpacking impacts of and reactions to pressures on the system, which adds value beyond some previous frameworks.