Research | Open Access | Volume 9 (3): Article 128 | Published: 05 Aug 2026
Menu, Tables and Figures
Table 1: Qualitative and quantitative attributes assessed, evaluation criteria and parameters, Mozambique, 2020 to 2023
Table 2: Number of patients ≥15 years of age, active on ART registered in the SISMA and SESP databases, Mozambique, 2020 to 2023
Table 3: Percentage difference of all patients ≥15 years of age registered in the SISMA and SESP databases active in ART, Mozambique, 2020 to 2023
Table 4: Percentage difference by sex of patients ≥15 years of age active on ART, registered in the SISMA and SESP databases, Mozambique, 2020 to 2023
Marta Zacarias Machava1,&, Maria Vilma Jossefa2, Erika Valeska Rossetto3, Áuria Ribeiro Banze3,4
1Field Epidemiology Training Program, National Institute of Health, Maputo, Mozambique, 2National Health Observatory, HIV Platform, National Institute of Health, Maputo, Mozambique, 3Field Epidemiology Training Program, Mozambique, 4Health Surveys and Observation Directorate, National Institute of Health, Maputo, Mozambique
&Corresponding author: Marta Zacarias Machava, Field Epidemiology Training Program, National Health Institute, Mozambique; Email: marta.machava@ins.gov.mz /martamachava26@gmail.com
Received: 04 Nov 2025, Accepted: 29 Jul 2026, Published: 04 Augr 2026
Domain: Infectious Disease Epidemiology
Keywords: HIV infections, Systems analysis, Mozambique; Statistical modelling; Epidemiological monitoring
©Marta Zacarias Machava et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Marta Zacarias Machava et al., Evaluating Spectrum model input data for Mozambique’s HIV care cascade, 2020–2023. Journal of Interventional Epidemiology and Public Health. 2026; 9(3):128. https://doi.org/10.37432/jieph-d-25-00272
Introduction: In 2023, the estimated HIV prevalence among individuals aged 15 years and older in Mozambique was 12.5%. The national response to the epidemic is monitored through HIV care and treatment cascade indicators for people living with HIV. The Spectrum model is widely used globally to estimate these indicators by integrating data from various sources, including routine health system reports. This study evaluated the input data used in the Spectrum model for Mozambique from 2020 to 2023.
Methods: We assessed the input data modelled in Spectrum to generate the HIV care cascade for individuals aged 15 years and older from 2020 to 2023. Qualitative and quantitative attributes of the data were evaluated using guidelines adapted from the United States Centers for Disease Control and Prevention.
Results: At the national level, the Monitoring and Evaluation Information System and Electronic Patient Registration System recorded 2,046,936 and 1,989,030 individuals on treatment, respectively, a 2.8% difference. Among men, the absolute difference was 6.9% (708,804 vs. 660,239), and among women, 0.7% (1,338,132 vs. 1,328,791). Median provincial-year inconsistency was 6.7% (range: 0.2–21.8%) for men and 2.5% (range: 0.0–11.1%) for women, with four provinces showing marked discrepancies. While the Spectrum tool is representative and timely, it lacks interoperability with routine data systems despite its adaptation to new input sources.
Conclusions: Although the data were valuable, the observed inconsistencies affected the indicators generated by the Spectrum model. Enhancements are recommended to improve the tool’s interoperability and flexibility, thereby accelerating the modelling of the HIV care cascade.
HIV remains a global public health threat. As of 2023, an estimated 39.9 million people worldwide were living with the infection, with Africa accounting for the highest burden, approximately 65% of all people living with HIV (PLHIV)[1]. By 2023, approximately 2.4 million people in Mozambique were living with HIV, with an estimated 44,000 deaths attributed to HIV/AIDS-related causes and around 81,000 new infections, corresponding to a prevalence rate of 12.5% [2].
The HIV epidemic is monitored using care and treatment cascade indicators, which are defined as follows: all individuals living with HIV should be aware of their HIV-positive status; all individuals who are aware of their HIV-positive status should be on antiretroviral therapy (ART); and all individuals receiving ART should achieve viral suppression, defined as a viral load of less than 1,000 copies/mL [3].
To guide the global response to the HIV epidemic, the Joint United Nations Programme on HIV/AIDS (UNAIDS), in collaboration with international partners, established 2025 targets: 95% of people living with HIV should know their HIV status; 95% of those who know their status should be receiving ART; and 95% of individuals on ART should achieve viral suppression. These targets are intended to eliminate HIV/AIDS as a public health threat by 2030[4].
Since 2002, Mozambique has utilised the Spectrum software, an internationally recognised tool developed by UNAIDS to model and triangulate data for estimating key indicators of the HIV epidemic [5].
Description of the Spectrum Modelling Tool
The Spectrum tool was implemented in Mozambique to generate estimates of HIV-related indicators, including those related to the care and treatment cascade. It provides insights into the dynamics of the epidemic and facilitates monitoring of the country’s progress toward achieving the 95-95-95 targets.
The HIV cascade indicators are modeled using data from multiple sources, including the National Survey on the Impact of HIV and AIDS in Mozambique (INSIDA), Demographic and Health Surveys (DHS), census data, HIV surveillance and epidemiological studies, as well as programmatic data from the Health Information System for Monitoring and Evaluation (SISMA), the Electronic Patient Record System (SESP), and the Electronic Laboratory Information Management System for Sample Management and Referral (DISA).
Modelling is carried out by the Multisectoral Technical Group (GTM), a body composed of representatives from various organizations established to support HIV monitoring efforts in the country. The GTM is led by the National Council for Combating AIDS (CNCS) in collaboration with the Ministry of Health (MOH), the National Institute of Health (INS), and the National Institute of Statistics (INE) [5].
The Spectrum model serves as the primary tool for generating national HIV estimates. The Naomi and Shiny90 models are integrated components, incorporated into Spectrum to produce estimates based on specific data types and stratification levels.
Spectrum generates national estimates of people living with HIV and, using programmatic data from SESP and SISMA, estimates the number of individuals who are aware of their HIV-positive status and receiving ART. Additionally, data from DISA are used to estimate the number of individuals on ART who have achieved viral suppression.
The GTM is responsible for manually collecting, reviewing, and entering data into the model, with the exception of Shiny90, for which the template is automatically imported into Spectrum. This modelling cycle begins in November of the current year and concludes in July of the following year, culminating in the release of national HIV estimates for the preceding year.
Once the data files are entered by GTM, the modelling process is conducted using mathematical algorithms within the Spectrum software. The resulting estimates are submitted to UNAIDS headquarters in Geneva for review. Subsequently, the estimates are forwarded to MoH in coordination with CNCS for national approval, followed by validation and formal publication by UNAIDS in Geneva.
Following validation, GTM undertakes backdating and reporting, and disseminates the results by email to MoH and CNCS, who are responsible for the national release of the estimates. Preliminary results are typically released in March, while final estimates are published by UNAIDS in July on the Global AIDS Update platform and at http://aidsinfo.unaids.org/, making them publicly accessible and initiating national dissemination efforts. Estimates for the viral suppression indicator are published only when the results exceed 50% of the expected threshold.
According to INSIDA 2021 estimates, among adults aged 15 years and older living with HIV, 71.6% were aware of their HIV-positive status, 69.0% were receiving ART, and 61.7% had achieved viral suppression [3]. Despite the implementation of various strategies, these estimates indicate that Mozambique continues to face significant challenges in ensuring access to health services.
No scientific evidence was identified evaluating the input data used in Spectrum modelling of the HIV care and treatment cascade for individuals aged 15 years and older in Mozambique. The reliability of HIV care cascade indicators depends on the quality of the input data used in the modelling process. Given that Spectrum is the primary tool guiding national HIV policy and resource allocation in Mozambique, evaluating the quality and functionality of its input data is crucial. This evaluation helps ensure the proper operation of the system and the generation of reliable estimates, which are essential for monitoring HIV care cascade indicators, informing evidence-based decision-making, guiding program planning and resource allocation, and tracking progress toward achieving the UNAIDS 95-95-95 targets.
We evaluated the modelling of input data in the Spectrum tool for the HIV care and treatment cascade among individuals aged 15 years and older from 2020 to 2023. The description of Spectrum was informed by the Spectrum Manual, terms of reference, and relevant reports.
Despite inherent differences between surveillance systems and the Spectrum modelling framework, we adapted the Centres for Disease Control and Prevention (CDC) guidelines for evaluating surveillance systems [6]. Flexibility, data quality, representativeness, timeliness, and usefulness are attributes described in the CDC evaluation guidelines[6]. Although these guidelines include additional attributes, the five selected were considered appropriate for evaluating the Spectrum model, considering the study objectives and the information available for their assessment. This evaluation included both qualitative attributes, such as data quality and system flexibility, and quantitative attributes, including representativeness and timeliness. The input data used in the Spectrum model were obtained from multiple sources, including demographic data (e.g., census), which provides detailed data on the demographic composition of the population.
HIV surveillance and epidemiological dynamics data include information on HIV incidence, prevalence, and findings from epidemiological studies. Population-based surveys, such as the DHS and the INSIDA, are considered the gold standard for HIV monitoring due to their comprehensive data on prevalence, risk behaviours, and healthcare access. Programmatic data from the SISMA and the SESP are used to inform modelling in years when survey data are unavailable. SISMA is the national health information platform for routine HIV indicators, including HIV testing, treatment initiation, retention, and viral load testing. SESP is a nationwide electronic patient-tracking system, though it is not yet fully implemented across all health facilities. DISA is a laboratory information system used to manage viral load data.
To evaluate data quality, we compared the number of patients on ART reported in SISMA and SESP. SESP captures individual-level, daily patient records, whereas SISMA compiles aggregated monthly summaries that are manually entered. Data quality was assessed by calculating the percentage difference in the number of active ART patients reported by the two systems, using the following formula.
$$ \text{Percent difference} = \frac{|\text{SISMA} – \text{SESP}|}{(\text{SISMA} – \text{SESP})/2} \times 100 $$
Attribute parameters were established through a literature review and prior experience of the research team.
We evaluate the flexibility of the system based on two criteria:
Adaptability to the integration of new data sources: This was evaluated through the incorporation of the INSIDA 2021 database, replacing the Immunization, Malaria, and HIV/AIDS Indicator Survey (IMASIDA 2015) for modelling purposes; Scoring: 0 to 1 (no adaptability: Not flexible (0); demonstrated adaptability: Flexible (1)
Interoperability with routine data systems (SISMA and SESP): This criterion examined the system’s ability to automatically import data without manual intervention; Scoring: 0 to 1 (no interoperability: Not flexible (0); Demonstrated interoperability: Flexible (1))
Maximum score for the flexibility attribute: 2 (≤ 1: Not flexible; 2: Flexible)
Representativeness was evaluated by verifying the percentage of health facilities represented by the system at the national level for each year. The parameters were created based on the Evaluation of the information system for monitoring and evaluating institutional maternal deaths that occurred between January 2017 and June 2019 in Maputo City [7]
Scoring: 0 to 1 (<95%: Not representative (0); ≥95%: Representative (1); Maximum score for the representativeness attribute: 1
We assessed the timeliness by checking the deadline set for releasing the results modelled by the system (July of each year), and the period in which they were released. This methodology was based on the Evaluation of the quality of data from the Live Birth Information System and the Mortality Information System for the neonatal period, Espírito Santo, Brazil, from 2007 to 2009 [8].
Scoring: 0 to 1 (Reports submitted after the deadline: Not timely (0); Reports submitted within the deadline: Timely (1)); Maximum score for the timeliness attribute: 1
We evaluated the usefulness of data from estimates released by checking the policies and strategies created in line with the estimates released during the period under analysis (2020-2024).
Scoring: 0 to 1 (Policies and strategies not created: Not useful (0); Policies and strategies created: Useful (1); Maximum score for the usefulness attribute: 1
The evaluation of the Spectrum tool revealed mixed performance across the five attributes assessed. While the system demonstrated acceptability, representativeness, timeliness, and usefulness, it lacked overall flexibility (Table 1).
Flexibility
The tool demonstrated adaptability in integrating the INSIDA 2021 database, which replaced the Immunization, Malaria, and HIV/AIDS Indicator Survey (IMASIDA 2015) as the basis for generating estimates. This parameter received a score of 1. However, the tool lacked interoperability with routine data systems such as SISMA and SESP, which provide essential inputs for modelling estimates. Data from these systems must be manually imported and integrated into the Spectrum software, resulting in a score of 0 for this attribute. With a total score of 1 out of a maximum possible score of 2, the tool was ultimately classified as not flexible (Table1).
Data quality
In 2023, at the national level, SISMA and SESP recorded 2,046,936 and 1,989,030 individuals aged 15 years and older receiving HIV treatment, respectively, reflecting a 2.8% difference. Among men, the absolute difference was 6.9% (708,804 vs. 660,239), while among women it was 0.7% (1,338,132 vs. 1,328,791). The median inconsistency by province and year (2020–2023) was 6.7% (range: 0.2%–21.8%) for men and 2.5% (range: 0.0%–11.1%) for women. These discrepancies were more pronounced in four provinces: Cabo Delgado (18.8%, range: 11.4%–21.8%), Maputo City (10.8%, range: 9.5%–13.4%), Zambezia (9.7%, range: 7.5%–12.0%), and Maputo Province (6.5%, range: 3.2%–12.6%) (Tables 2 and 4). Based on absolute differences, the attribute received a score of 2 for both sexes, classifying it as acceptable (Table 1).
Representativeness
Across all years analyzed, the model demonstrated comprehensive coverage of health facilities providing HIV care and treatment services nationwide. In 2020, all 1,633 facilities were included, reflecting 100% coverage. This full coverage was maintained in subsequent years: 1,706 out of 1,706 facilities in 2021, 1,725 out of 1,725 in 2022, and 1,753 out of 1,753 in 2023. Based on these results, the parameter received a score of 1 and was classified as representative (Table 1).
Opportunity
Between 2020 and 2023, annual estimates were published on time (by July), with the exception of 2022. That year, the estimates were not approved due to concerns regarding data quality. A misalignment was identified between programmatic data sources and INSIDA figures. Notably, the Prenatal consultation data, an essential input for the Spectrum tool, exhibited inconsistencies, including reported coverage rates exceeding 100% and a higher number of pregnant women than recorded births. These discrepancies compromised the reliability of the Spectrum projections, resulting in the exclusion of this dataset from the modelling process and the non-publication of 2021 estimates in 2022. Consequently, the estimates were considered untimely, limiting the availability of up-to-date data for decision-making during that year. Despite this single-year delay, the overall timeliness of the system was evaluated based on its adherence to the established deadline in the years when estimates were released and was therefore classified as timely (Table 1).
Usefulness of data
Based on estimates generated by INSIDA 2021, the Government of Mozambique supported the development and implementation of the “Know Your State Status” campaign, aimed at enhancing access to HIV diagnosis and promoting timely initiation of treatment to achieve viral suppression[3]. In response to estimates, the PEN-V was developed with strategies aimed at increasing testing coverage and achieving the 95-95 target by 2025[9]. Actions were undertaken to develop the National HIV Prevention Roadmap 2022–2025, which includes a long-term vision extending to 2030. The roadmap is structured around key pillars, such as prevention through diagnosis, to support achievement of the 95-95-95 targets [9].
We assessed the modelling of input data in the Spectrum tool by evaluating both qualitative and quantitative attributes to determine whether the system fulfils its intended objectives. Although the tool successfully integrated the INSIDA dataset, it lacked overall flexibility due to the absence of interoperability with routine data sources used for modelling. Interoperability enables the seamless exchange of information between otherwise disconnected systems, facilitating integration and enhancing system adaptability [10]. Interoperability increases efficiency and effectiveness, reducing operating time and costs, as well as ensuring data consistency and effective communication between different platforms [11].
The data revealed inconsistencies, which were particularly pronounced in the provinces of Cabo Delgado, Maputo City, Zambezia, and Maputo Province. These findings are consistent with those observed in a Ugandan health information system, where large volumes of data from multiple sources were manually entered, leading to data inconsistencies and technical challenges in system harmonisation [12]. Challenges associated with fragmented manual data entry highlighted the need for interoperability between systems to enhance overall effectiveness and efficiency [10].
Among the years analyzed (2020–2023), the tool released estimates within the designated timeframe in three of the four years. However, in 2022, the estimates were not available in time to support decision-making. To address this, the modeling process was realigned using INSIDA 2021 data. Accordingly, the timeliness of the system was evaluated based on adherence to established deadlines in the years when estimates were released.
It is important to note that the timing of estimate releases is not aligned with the national strategic planning cycle, which occurs annually, typically at the end of the first half of the year. As such, the production, management, and dissemination of information are critical components for reinforcing the system’s objectives and enhancing the effectiveness and timeliness of public health response efforts [13]. The data were representative, as collection encompassed all health facilities providing ART. This attribute is among the most critical in the evaluation, as it enables assessment of the tool’s scope. In this case, the tool demonstrated complete national coverage, with future efforts focused on maintaining comprehensive inclusion of all ART-providing facilities across all years.
Over the years evaluated, the data proved instrumental in informing strategies to control the HIV epidemic. Integrated approaches have focused on increasing the proportion of people living with HIV who are aware of their status, strengthening their linkage to health services, ensuring continuity of treatment, and achieving viral suppression.
Limitations
In evaluating the modelling of input data, several limitations were identified that may impact the interpretation and reliability of the study’s conclusions. Other CDC evaluation attributes were not assessed because they did not apply to the Spectrum modelling framework, while some could not be evaluated due to limitations in the available data sources. The exhaustiveness of the data was not evaluated, as the available database contained indicators without corresponding variables, with data provided only in aggregate form. Consequently, the completeness of the data was not assessed, which means that it cannot be guaranteed that the available data fully represent the reality of the analyzed context. The data used to assess consistency were not comprehensive across all indicators in the HIV care and treatment cascade; only data on individuals living with HIV who were on ART and actively receiving care were included, the analysis does not fully reflect the situation of all people living with HIV, only those on ART.
Furthermore, the methodology used to assess representativeness was not exhaustive, despite the Spectrum modelling process being intended to estimate the cascade for the national population of Mozambique. It is important to highlight that Spectrum can generate representative estimates based on sources beyond programmatic data collected at health facilities, including national surveys, epidemiological studies, and data from specific population groups. These additional sources were not included in the evaluation, which limits the ability to verify whether the estimates adequately reflect the national reality.
This is the first assessment of input data modeling for generating HIV care and treatment cascade estimates using the Spectrum tool in Mozambique. Currently, there is no official platform for disseminating GTM reports to the Ministry of MoH and the CNCS. The modeling tool is not flexible. Although the data were considered acceptable and useful, observed inconsistencies affected the reliability of indicators produced by Spectrum. These findings underscore the need for continued monitoring and evaluation of routine data, enhanced interoperability between systems, and the availability of high-quality data to support assessments of data quality and representativeness in future studies.
Recommendations
For INS, MH, CNCS: Promote periodic evaluation studies of the modelling process conducted using the tool, with an emphasis on allocating additional resources to assess the quality of input data used to generate estimates, as well as to evaluate data representativeness
For UNAIDS, NH and CNCS: Establish a formal website for sharing estimates and underlying assumptions between the GTM and the Ministry of Health (MOH), serving as a centralized repository and historical archive for future reference and consultation.
For UNAIDS: Enhance the tool’s interoperability with routine data systems that support estimate modelling to improve operational efficiency and ensure alignment of dissemination timelines with the national planning calendar.
What is already known about the topic
What this study adds
We express our gratitude to Dr. Makini Boothe for providing the data and valuable support in the preparation of this article. We also thank the coordinators of the Field Epidemiology Training Program (FETP) for their guidance throughout the development of the manuscript. Appreciation is extended to the team at MOH, particularly Orrin, for supplying the data necessary to evaluate the representativeness attribute. Special thanks are due to the HIV platform team Fábio Ponda, Ana Mutola, and Cacilda Fumo for their continued support.
This study has been supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centers for Disease Control and Prevention (CDC) under the terms of NU2GGH002472. The findings and conclusions in this paper are those of the author(s) and do not necessarily represent the official position of the funding agencies.
| Attribute | Assessment criteria | Results found |
|---|---|---|
| Flexibility | case of adaptation when changing the inputs integrated into the modeling tool Has the ability to adapt (1) Does not have the ability to adapt (0) | Has the ability to adapt (1) Classification: flexible |
| Interoperability with other systems Interoperability with other systems: flexible (1) No interoperability with other systems: not flexible (0) | No interoperability with other systems (0) Rating: Not flexible Final rating: Not flexible | |
| Data quality | Data consistency: Percentage difference between active patients on ART in the SISMA* database compared to SESP** Differences: <10% Acceptable (2) From 10% to 20%: fair (1) > 20% Low quality (0) | 6.7% inconsistency (range=0.2–21.8%) in men 2.5% inconsistency (range=0.0–11.1%) in women Classification: Acceptable |
| Representativ eness | Percentage of health units represented by the system at national level per year ≥95% Representative (1) <95% non-representative (0) | All health units per year were represented by the system (100%) Classification: Representative |
| Opportunity | Annual reports within the deadline established by the system (July of each year) Report within the deadline: timely (1) Report after the deadline: not timely (0) | Results reported on time, except for 2022, which did not have publication in 2021 Classification: Timely |
| All adults | SISMA | SESP | ||||||
|---|---|---|---|---|---|---|---|---|
| Provinces | 2020 | 2021 | 2022 | 2023 | 2020 | 2021 | 2022 | 2023 |
| Niassa | 33,884 | 43,147 | 52,241 | 61,913 | 32,959 | 42,359 | 50,295 | 59,425 |
| Cabo Delgado | 65,798 | 94,264 | 112,989 | 128,902 | 65,337 | 85,093 | 100,882 | 121,408 |
| Nampula | 141,169 | 197,595 | 250,339 | 272,890 | 135,195 | 191,075 | 244,905 | 265,114 |
| Zambézia | 268,190 | 331,874 | 389,216 | 437,501 | 271,883 | 340,701 | 399,698 | 424,930 |
| Tete | 72,983 | 88,183 | 104,866 | 121,983 | 74,110 | 88,993 | 105,128 | 121,387 |
| Manica | 89,953 | 111,742 | 133,748 | 152,439 | 94,375 | 115,730 | 133,747 | 151,982 |
| Sofala | 109,290 | 140,610 | 174,360 | 192,555 | 118,643 | 147,667 | 178,699 | 194,115 |
| Inhambane | 79,224 | 90,214 | 100,358 | 110,236 | 79,301 | 88,929 | 99,353 | 109,842 |
| Gaza | 161,653 | 174,619 | 190,524 | 203,679 | 157,413 | 169,028 | 182,496 | 196,448 |
| Maputo Province | 154,197 | 172,764 | 195,259 | 195,811 | 153,789 | 166,731 | 173,898 | 181,250 |
| Maputo City | 143,479 | 154,305 | 165,498 | 169,027 | 147,549 | 155,245 | 161,585 | 166,134 |
| Mozambique | 1,319,820 | 1,599,317 | 1,869,398 | 2,046,936 | 1,330,554 | 1,591,551 | 1,830,686 | 1,993,421 |
*SISMA: Monitoring and Evaluation Information System; *SESP: Electronic Patient Record System
| Provence | 2020 | 2021 | 2022 | 2023 | Median by province by year |
|---|---|---|---|---|---|
| Niassa | 2.80% | 1.80% | 3.80% | 4.30% | 3.30% |
| Cabo Delgado | 0.70% | 10.20% | 11.30% | 6.50% | 8.40% |
| Nampula | 4.30% | 3.40% | 2.20% | 4.50% | 3.80% |
| Zambézia | 1.40% | 2.60% | 2.70% | 2.80% | 2.60% |
| Tete | 1.50% | 0.90% | 0.20% | 0.10% | 0.60% |
| Manica | 4.80% | 3.50% | 0.00% | 0.80% | 2.20% |
| Sofala | 8.20% | 4.90% | 2.50% | 1.90% | 3.70% |
| Inhambane | 0.10% | 1.40% | 1.00% | 0.80% | 0.90% |
| Gaza | 2.70% | 3.30% | 4.30% | 3.80% | 3.50% |
| Maputo Province | 0.30% | 3.60% | 11.60% | 7.40% | 5.50% |
| Maputo City | 2.80% | 0.60% | 2.40% | 2.00% | 2.20% |
| Median by province by year | 2.70% | 3.30% | 2.50% | 2.80% | 2.70% |
| Difference at national level | 0.80% | 0.50% | 2.10% | 2.80% |
*SISMA: Monitoring and Evaluation Information System; SESP: Electronic Patient Record System
| Sex | Province | 2020 | 2021 | 2022 | 2023 | Median by province by year |
|---|---|---|---|---|---|---|
| Masculine | Niassa | 6% | 4.9% | 6.3% | 5.7% | 5.8% |
| Cabo Delgado | 16% | 21.8% | 21.8% | 11.4% | 18.8% | |
| Nampula | 9% | 6.6% | 7.0% | 8.1% | 7.5% | |
| Zambézia | 11% | 8.3% | 7.5% | 12.0% | 9.7% | |
| Tete | 3% | 2.1% | 0.7% | 0.2% | 1.4% | |
| Manica | 2% | 8.3% | 2.1% | 2.9% | 2.5% | |
| Sofala | 5% | 3.6% | 1.5% | 2.3% | 2.9% | |
| Inhambane | 6% | 5.9% | 4.4% | 3.4% | 5.1% | |
| Gaza | 8.4% | 7.5% | 7.8% | 7.0% | 7.6% | |
| Maputo Province | 3.2% | 6.2% | 12.6% | 6.8% | 6.5% | |
| Maputo City | 9.5% | 10.5% | 13.4% | 11.2% | 10.8% | |
| Median by province by year | 6.0% | 6.6% | 7.0% | 6.8% | 6.7% | |
| Difference at national level | 6.1% | 6.8% | 7.2% | 6.9% | ||
| Female | Province | Median per year by province | ||||
| Niassa | 1.3% | 0.4% | 2.6% | 3.5% | 1.9% | |
| Cabo Delgado | 7.4% | 3.9% | 5.4% | 3.8% | 4.7% | |
| Nampula | 1.9% | 1.6% | 0.5% | 2.5% | 1.7% | |
| Zambézia | 7.7% | 8.6% | 8.4% | 2.6% | 8.1% | |
| Tete | 0.9% | 0.3% | 0.0% | 0.2% | 0.3% | |
| Manica | 6.3% | 9.7% | 1.1% | 0.2% | 3.7% | |
| Sofala | 9.6% | 5.5% | 2.9% | 1.6% | 4.2% | |
| Inhambane | 1.9% | 0.0% | 0.1% | 0.0% | 0.1% | |
| Gaza | 0.3% | 1.5% | 2.9% | 2.4% | 2.0% | |
| Maputo Province | 1.0% | 2.3% | 11.1% | 7.6% | 5.0% | |
| Maputo City | 9.0% | 6.2% | 3.3% | 2.7% | 4.7% | |
| Median by province by year | 1.9% | 2.3% | 2.9% | 2.5% | 2.5% | |
| Difference at national level | 4.2% | 2.7% | 0.6% | 0.7% | ||
*SISMA: Monitoring and Evaluation Information System; *SESP: Electronic Patient Record System