Mathematical Lens Reveals Hidden Waste in Pakistan’s Public Health Clinics

In the sprawling province of Punjab, home to more than half of Pakistan’s population, the public health system operates under relentless pressure. Clinics and hospitals are expected to deliver essential services, including family planning, to millions of people with limited staff, limited supplies, and limited budgets. A new study published in BMC Health Services Research has now applied a rigorous mathematical technique to a question that has long frustrated health planners: are these facilities actually converting the resources they receive into services, or are precious inputs quietly leaking away? The answer, drawn from one hundred public health facilities across the province, is a mixture of reassurance and warning. Facilities are performing reasonably well on average, but significant pockets of inefficiency persist, and they are concentrated precisely in the rural institutions that serve some of the most underserved communities.

The research team, led by Ain ul Momina of King Edward Medical University in Lahore and including collaborators from the University of the Punjab, the University of Cambridge, and Charité Universitätsmedizin Berlin, focused on family planning service delivery. This is not a niche concern. Pakistan has long struggled with a contraceptive prevalence rate that remains stubbornly below the levels needed to meet demand, and the national and provincial governments have invested heavily in programs designed to accelerate access to modern contraception. One such initiative, the Delivering Accelerated Family Planning in Pakistan project, known as DAFPAK, has supported facilities in expanding services. Yet whether that additional support translates into genuinely better performance has been difficult to measure with conventional indicators alone, which is where the study’s methodological approach becomes important.

The technique at the heart of the study is data envelopment analysis, or DEA, a non-parametric operations research method that has become a workhorse of health systems research worldwide. Rather than comparing each facility to a fixed benchmark or an average, DEA constructs a frontier of best performers, the so-called efficient frontier, and measures every other facility by its distance from that frontier. Each clinic, hospital, or health unit is treated as a decision-making unit that consumes inputs, in this case contraceptive stock, staff availability, and facility size, and produces outputs in the form of services delivered. A facility that achieves the maximum output from its given inputs is rated as fully efficient, with a score of one hundred percent, while a facility producing less than it could with the same resources receives a proportionally lower score. The method’s appeal lies in its ability to handle multiple inputs and outputs simultaneously without requiring the researcher to specify in advance how each factor should be weighted.

The study’s design was deliberately two-stage. In the first stage, DEA was applied to data collected from one hundred public health facilities managed by the Primary and Secondary Health Department of Punjab. Half of these facilities belonged to the DAFPAK cohort and half did not, allowing the researchers to examine whether participation in the accelerated family planning program was associated with different efficiency profiles. Data were gathered through computer-assisted personal interviews and drawn from electronic medical records, a choice that streamlined collection and, because the records system already incorporates patient permission at registration, meant that separate consent procedures were not required. Ethics approval came from the Departmental Doctoral Program Ethical Committee of Public Health at the University of the Punjab’s Institute of Social and Cultural Studies.

The headline result is that the average technical efficiency score across the one hundred facilities stood at seventy-eight percent. In practical terms, this suggests that the typical facility could, in principle, deliver roughly the same level of family planning services with about twenty-two percent fewer inputs, or conversely, expand its output by a similar margin without consuming any additional resources. In a province where every rupee and every trained health worker counts, that gap represents a substantial hidden reserve of capacity. The researchers emphasize that DEA does not merely rank facilities; it identifies specific peers against which underperforming units can be compared, offering managers a concrete picture of what better performance looks like in comparable settings rather than an abstract target.

Beneath the average, however, the picture becomes more uneven. The study found significant variations across facility types, with Basic Health Units and Rural Health Centers showing particularly pronounced resource optimization gaps. These are the front line of Punjab’s rural health system, the smallest and most numerous institutions, often staffed by a handful of workers and serving dispersed populations. The finding that they lag in efficiency is doubly consequential. First, it means that the resources allocated to rural primary care are not stretching as far as they could. Second, because these facilities are often the only accessible point of care for rural women seeking contraception, inefficiency at this level translates directly into unmet need among the very populations that family planning programs most aim to reach.

The second stage of the analysis tackled a question that first-stage DEA alone cannot answer: why do some facilities perform better than others? Efficiency scores are influenced not only by how a facility manages its own resources but also by its surrounding environment, factors beyond the control of any individual manager. To disentangle these effects, the researchers employed the Simar-Wilson regression approach, a statistical method specifically developed for the truncated nature of efficiency scores produced by DEA. Ordinary regression techniques are ill-suited to such scores, which are bounded between zero and one, and the Simar-Wilson procedure corrects for this, yielding more reliable estimates of how environmental variables shape performance.

One environmental factor stood out. The analysis revealed a negative association between the number of other healthcare providers in a facility’s catchment area and that facility’s efficiency. In other words, clinics operating in areas crowded with alternative providers, whether public, private, or non-governmental, tended to score lower on technical efficiency than clinics serving areas with fewer competing sources of care. The interpretation offered by the researchers is instructive: service provision needs to be aligned with local demand. When multiple providers cluster in the same catchment, each may draw a smaller share of patients, leaving facilities with staff and supplies that are underutilized relative to their capacity. This is a planning problem as much as a management one, and it points toward smarter geographic allocation of resources rather than simply adding more facilities or more stock in areas that are already well served.

The study’s implications extend well beyond Punjab. Resource-constrained health systems around the world face the same fundamental challenge of deciding where to direct scarce funds, and the two-stage DEA framework offers a template for answering that question with data rather than intuition. For Pakistan specifically, the findings arrive at a moment when family planning remains a critical lever for maternal health, child survival, and broader development goals. The authors conclude that DEA effectively identifies inefficiencies and can inform resource optimization in public health facilities, and they argue that their results offer valuable insights for improving family planning services and healthcare delivery in settings where every input must count. The research received no specific grant from any funding agency, and the underlying data came from facilities’ own electronic records, giving the analysis a grounded, operational character.

What makes the study compelling is its refusal to accept averages as the end of the story. A seventy-eight percent efficiency score sounds respectable until one recognizes that it conceals a distribution in which rural Basic Health Units and Rural Health Centers fall well short of their potential. By pairing a frontier-based efficiency measurement with a statistically appropriate second-stage regression, the researchers have converted a routine performance audit into a diagnostic tool that tells policymakers not only where the waste is but also what surrounds it. Aligning service provision with local demand, as the negative association with provider density suggests, may prove as important as any internal reform. For the millions of women in Punjab whose access to contraception depends on the performance of a small rural clinic, that insight could make the difference between a health system that merely functions and one that truly delivers.

Subject of Research: Technical efficiency of public health facilities delivering family planning services in Punjab, Pakistan, assessed using two-stage data envelopment analysis

Article Title: Technical efficiency of public health facilities in Punjab, Pakistan: application of a two-stage data envelopment analysis

Article References: Momina, A. U., Ahmad, T., Zakar, R., Shahzad, S., Ahmad, F., Mahmood, S., & Fischer, F. (2026). Technical efficiency of public health facilities in Punjab, Pakistan: application of a two-stage data envelopment analysis. BMC Health Services Research, 26(1), Article 1397. https://doi.org/10.1186/s12913-026-15847-7

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15847-7

Keywords: data envelopment analysis, technical efficiency, public health facilities, family planning, Punjab, Pakistan, contraceptive prevalence, DAFPAK, Simar-Wilson regression, health services research, Basic Health Units, resource optimization